{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\Data Replication File Materials (JPART)\Output\Administrative Bias.MANUSCRIPT.02-01-2026.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 1 Feb 2026, 15:10:35
{txt}
{com}. 
. 
. 
. **** MANUSCRIPT STATISTICAL ANALYSES [JULY 7, 2025] ****
. 
. 
. * MODELS 1 & 3 BASED ON FULL SAMPLE OF OBSERVATIONS *
. 
. 
. * MODELS 2 & 4 BASED ON RESTRICTED SAMPLE OF OBSERVATIONS -- OMITTING OBSERVATIONS CONTAINED IN BASELINE NON-GUBERNATORIAL APPOINTMENT AUTHORITY CATEGORY [APPROVAL, BUT NO DIRECT GUBERNATORIAL APPOINTMENT AUTHORITY (N = 42) & NON-PARTISAN GOVERNOR OBSERVATIONS (N = 5)
. 
. 
. 
. 
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. 
. *** Retrieve UPDATED Statistical Database with "NEW VARIBALES" Incorporated as of 07-04-2025 *** 
. 
. use "C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\Data Replication File Materials (JPART)\Data\Admin_Bias.MASTER DATABASE.07-04-2025.dta", clear
{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. *** SET DATA TO PANEL STRUCTURE  ***
. 
. xtset state_numident year, yearly
{res}
{col 1}{txt:Panel variable: }{res:state_numident}{txt: (strongly balanced)}
{p 1 16 2}{txt:Time variable: }{res:year}{txt:, }{res:{bind:2002}}{txt: to }{res:{bind:2021}}{p_end}
{txt}{col 10}Delta: {res}1 year
{txt}
{com}. 
. *
. *
. *
. 
. 
. 
. * Compute Natural Logarithm of Dependent Variable [Benefit Overpayment Error Detection -- in 2010 Constant Dollars] for ancillary bivariate correlations with 'levels' measure *
. 
. 
. * NOTE: "SAI" refers to State Agency Initiated Detection of Benefit Overpayment Errors -- which comprise, on mean average, 98.6% of all such cases & 97.4% median average of total amounts detected for all such cases (the remaining 1.4% & 2.6% are captured by BAM sample estimates)  
. 
. gen ln_sai_detamt_clmtreal = ln(sai_detamt_clmtreal + 1)
{txt}
{com}. 
. 
. ** GENERATE ADDITIONAL 'SCALE' EFFECT CONTROL MEASURES BASED ON BAM QUALITY CONTROL SURVEY SAMPLING ESTIMATES OF TOTAL ADMINISTRATIVE ERRORS BY STATE AGENCY IN A GIVEN YEAR & TOTAL EMPLOYER APPEALS OF CLAIMANT BENEFITS **
. 
. gen ln_clmterror_est = ln(clmterror_est + 1)
{txt}(1 missing value generated)

{com}. 
. gen ln_employerappeals_ct = ln(employerappeals_ct + 1)
{txt}
{com}. 
. 
. 
. drop if missing(clmterror_est)
{txt}(1 observation deleted)

{com}. *
. drop if missing(employerappeals_ct)
{txt}(0 observations deleted)

{com}. 
. 
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *** DESCRIPTIVE STATISTICS FOR DEPENDENT VARIABLES EMPLOYED IN THIS STUDY: CLAIMANT OVERPAYMENTS [IN CONSTANT 2010 DOLLARS]: BOTH FULL SAMPLE AND THOSE OMITTING NON-PARTISAN GUBERNATORIAL APPROVAL APPOINTMENT AUTHORITY [SEE DESCRIPTION ABOVE ON LINE [IN CONSTANT 2010 DOLLARS] ***
. 
. sum  sai_detamt_clmtreal, detail

                     {txt}sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res} 261998.5       247.3008
{txt} 5%    {res} 447386.9       39267.18
{txt}10%    {res} 677182.5       137599.1       {txt}Obs         {res}        999
{txt}25%    {res}  1580789       139155.7       {txt}Sum of wgt. {res}        999

{txt}50%    {res}  3787030                      {txt}Mean          {res} 1.20e+07
                        {txt}Largest       Std. dev.     {res} 2.86e+07
{txt}75%    {res} 1.00e+07       2.39e+08
{txt}90%    {res} 3.01e+07       2.45e+08       {txt}Variance      {res} 8.17e+14
{txt}95%    {res} 5.17e+07       2.49e+08       {txt}Skewness      {res} 7.642898
{txt}99%    {res} 1.34e+08       4.74e+08       {txt}Kurtosis      {res}  90.6999
{txt}
{com}. *
. sum  sai_detamt_clmtreal if nonpartisan_gubapprove!=1, detail

                     {txt}sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res} 261998.5       247.3008
{txt} 5%    {res} 440988.9       39267.18
{txt}10%    {res} 656214.6       137599.1       {txt}Obs         {res}        952
{txt}25%    {res}  1562214       139155.7       {txt}Sum of wgt. {res}        952

{txt}50%    {res}  3914010                      {txt}Mean          {res} 1.24e+07
                        {txt}Largest       Std. dev.     {res} 2.92e+07
{txt}75%    {res} 1.04e+07       2.39e+08
{txt}90%    {res} 3.16e+07       2.45e+08       {txt}Variance      {res} 8.53e+14
{txt}95%    {res} 5.30e+07       2.49e+08       {txt}Skewness      {res} 7.472585
{txt}99%    {res} 1.34e+08       4.74e+08       {txt}Kurtosis      {res} 86.83069
{txt}
{com}. *
. *
. *
. *
. 
. ** DESCRIPTIVE STATISTICS AND BIVARIATE CORRELATION BETWEEN 'SAI' OUTCOME/DEPENDENT VARIABLE MEASURE AND 'TOTAL' VERSION OF THIS MEASURE **
. 
. sum sai_detamt_clmtreal detamt_clmtreal, detail

                     {txt}sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res} 261998.5       247.3008
{txt} 5%    {res} 447386.9       39267.18
{txt}10%    {res} 677182.5       137599.1       {txt}Obs         {res}        999
{txt}25%    {res}  1580789       139155.7       {txt}Sum of wgt. {res}        999

{txt}50%    {res}  3787030                      {txt}Mean          {res} 1.20e+07
                        {txt}Largest       Std. dev.     {res} 2.86e+07
{txt}75%    {res} 1.00e+07       2.39e+08
{txt}90%    {res} 3.01e+07       2.45e+08       {txt}Variance      {res} 8.17e+14
{txt}95%    {res} 5.17e+07       2.49e+08       {txt}Skewness      {res} 7.642898
{txt}99%    {res} 1.34e+08       4.74e+08       {txt}Kurtosis      {res}  90.6999

               {txt}detamt_clmt in constant dollars
{hline 61}
      Percentiles      Smallest
 1%    {res}   307367       43973.57
{txt} 5%    {res} 490090.5       80218.74
{txt}10%    {res} 765253.3       195672.4       {txt}Obs         {res}        999
{txt}25%    {res}  1659369       195829.6       {txt}Sum of wgt. {res}        999

{txt}50%    {res}  3886146                      {txt}Mean          {res} 1.20e+07
                        {txt}Largest       Std. dev.     {res} 2.86e+07
{txt}75%    {res} 1.02e+07       2.39e+08
{txt}90%    {res} 3.02e+07       2.45e+08       {txt}Variance      {res} 8.19e+14
{txt}95%    {res} 5.18e+07       2.49e+08       {txt}Skewness      {res} 7.634266
{txt}99%    {res} 1.34e+08       4.74e+08       {txt}Kurtosis      {res} 90.46983
{txt}
{com}. *
. correlate sai_detamt_clmtreal detamt_clmtreal
{txt}(obs=999)

             {c |} sai_de~l de~treal
{hline 13}{c +}{hline 18}
sai_detamt~l {c |}{res}   1.0000
{txt}detamt_clm~l {c |}{res}   1.0000   1.0000

{txt}
{com}. correlate sai_detamt_clmtreal detamt_clmtreal if nonpartisan_gubapprove!=1
{txt}(obs=952)

             {c |} sai_de~l de~treal
{hline 13}{c +}{hline 18}
sai_detamt~l {c |}{res}   1.0000
{txt}detamt_clm~l {c |}{res}   1.0000   1.0000

{txt}
{com}. *
. *
. 
. * Percentage & Difference Calculations Between Median 'SAI'  & ' Total' Outcome Measures [Full Sample] * 
. 
. display (3787030/3886146)*100
{res}97.449504
{txt}
{com}. *
. display (3886146 - 3787030)/3886146
{res}.02550496
{txt}
{com}. 
. 
{txt}end of do-file

{com}. do "C:\Users\hongj\AppData\Local\Temp\STD1b3c_000004.tmp"
{txt}
{com}. 
. 
. 
. *** NOTE: NEED TO EMPLOY LOGNORMAL REGRESSION SINCE CORRELATION WITH LOGGED TRANSFORMED DATA IS NOT STRONGLY CORRELATED [also log-normal better for heavily right-skewed data than gamma] ***
. 
. correlate ln_sai_detamt_clmtreal sai_detamt_clmtreal
{txt}(obs=999)

             {c |} ln_sai~l sai_de~l
{hline 13}{c +}{hline 18}
ln_sai_det~l {c |}{res}   1.0000
{txt}sai_detamt~l {c |}{res}   0.6315   1.0000

{txt}
{com}. * 
. correlate ln_sai_detamt_clmtreal sai_detamt_clmtreal if nonpartisan_gubapprove!=1
{txt}(obs=952)

             {c |} ln_sai~l sai_de~l
{hline 13}{c +}{hline 18}
ln_sai_det~l {c |}{res}   1.0000
{txt}sai_detamt~l {c |}{res}   0.6312   1.0000

{txt}
{com}. 
. 
. 
. 
. 
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** COMPUTE WITHIN-STATE DESCRIPTIVE STATISTICS SINCE MODEL ESTIMATES ARE WITHIN-STATE EFFECTS [FULL MODEL OF OBSERVATIONS] *** 
. 
. ** compute within-state descriptive statistics for outcome variables [detamt_clmtreal & detamt_empreal] [FULL AND RESTRICTED SAMPLES] *
. 
. * calculate the state group-means *
. 
. egen b_sai_detamt_clmtreal = mean(sai_detamt_clmtreal), by(state_numident)
{txt}
{com}. *
. egen b_sai_detamt_clmtreal_omit = mean(sai_detamt_clmtreal) if nonpartisan_gubapprove!=1, by(state_numident)
{txt}(47 missing values generated)

{com}. *
. *
. *
. 
. * compute the within-state deviations from the respective state group means [FULL AND RESTRICTED SAMPLES] *
. 
. gen w_sai_detamt_clmtreal = sai_detamt_clmtreal - b_sai_detamt_clmtreal
{txt}
{com}. *
. gen w_sai_detamt_clmtreal_omit = sai_detamt_clmtreal - b_sai_detamt_clmtreal_omit if nonpartisan_gubapprove!=1
{txt}(47 missing values generated)

{com}. *
. *
. *
. 
. 
. * compute descriptive statistics & correlations for overall measure and within-state measures for each dependent variable [FULL AND RESTRICTED SAMPLES] *
.  
. sum sai_detamt_clmtreal  w_sai_detamt_clmtreal, detail

                     {txt}sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res} 261998.5       247.3008
{txt} 5%    {res} 447386.9       39267.18
{txt}10%    {res} 677182.5       137599.1       {txt}Obs         {res}        999
{txt}25%    {res}  1580789       139155.7       {txt}Sum of wgt. {res}        999

{txt}50%    {res}  3787030                      {txt}Mean          {res} 1.20e+07
                        {txt}Largest       Std. dev.     {res} 2.86e+07
{txt}75%    {res} 1.00e+07       2.39e+08
{txt}90%    {res} 3.01e+07       2.45e+08       {txt}Variance      {res} 8.17e+14
{txt}95%    {res} 5.17e+07       2.49e+08       {txt}Skewness      {res} 7.642898
{txt}99%    {res} 1.34e+08       4.74e+08       {txt}Kurtosis      {res}  90.6999

                    {txt}w_sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res}-4.12e+07      -5.81e+07
{txt} 5%    {res}-1.89e+07      -5.71e+07
{txt}10%    {res} -8105136      -5.59e+07       {txt}Obs         {res}        999
{txt}25%    {res} -2426790      -5.45e+07       {txt}Sum of wgt. {res}        999

{txt}50%    {res}-596541.3                      {txt}Mean          {res} .0078829
                        {txt}Largest       Std. dev.     {res} 2.15e+07
{txt}75%    {res}   590808       1.61e+08
{txt}90%    {res}  3605272       1.85e+08       {txt}Variance      {res} 4.62e+14
{txt}95%    {res}  9631936       1.89e+08       {txt}Skewness      {res} 9.119806
{txt}99%    {res} 8.17e+07       3.96e+08       {txt}Kurtosis      {res} 138.9647
{txt}
{com}. correlate sai_detamt_clmtreal  w_sai_detamt_clmtreal
{txt}(obs=999)

             {c |} sai_de~l w_sai_~l
{hline 13}{c +}{hline 18}
sai_detamt~l {c |}{res}   1.0000
{txt}w_sai_deta~l {c |}{res}   0.7519   1.0000

{txt}
{com}. *
. *
. sum sai_detamt_clmtreal  w_sai_detamt_clmtreal_omit if nonpartisan_gubapprove!=1, detail

                     {txt}sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res} 261998.5       247.3008
{txt} 5%    {res} 440988.9       39267.18
{txt}10%    {res} 656214.6       137599.1       {txt}Obs         {res}        952
{txt}25%    {res}  1562214       139155.7       {txt}Sum of wgt. {res}        952

{txt}50%    {res}  3914010                      {txt}Mean          {res} 1.24e+07
                        {txt}Largest       Std. dev.     {res} 2.92e+07
{txt}75%    {res} 1.04e+07       2.39e+08
{txt}90%    {res} 3.16e+07       2.45e+08       {txt}Variance      {res} 8.53e+14
{txt}95%    {res} 5.30e+07       2.49e+08       {txt}Skewness      {res} 7.472585
{txt}99%    {res} 1.34e+08       4.74e+08       {txt}Kurtosis      {res} 86.83069

                 {txt}w_sai_detamt_clmtreal_omit
{hline 61}
      Percentiles      Smallest
 1%    {res}-4.12e+07      -5.81e+07
{txt} 5%    {res}-1.99e+07      -5.71e+07
{txt}10%    {res} -8690952      -5.59e+07       {txt}Obs         {res}        952
{txt}25%    {res} -2583069      -5.45e+07       {txt}Sum of wgt. {res}        952

{txt}50%    {res}-616210.1                      {txt}Mean          {res}-.0110294
                        {txt}Largest       Std. dev.     {res} 2.20e+07
{txt}75%    {res} 590394.5       1.61e+08
{txt}90%    {res}  3692663       1.85e+08       {txt}Variance      {res} 4.85e+14
{txt}95%    {res} 1.06e+07       1.89e+08       {txt}Skewness      {res} 8.907209
{txt}99%    {res} 8.17e+07       3.96e+08       {txt}Kurtosis      {res} 132.5189
{txt}
{com}. correlate sai_detamt_clmtreal  w_sai_detamt_clmtreal_omit if nonpartisan_gubapprove!=1
{txt}(obs=952)

             {c |} sai_de~l w_sai_~t
{hline 13}{c +}{hline 18}
sai_detamt~l {c |}{res}   1.0000
{txt}w_sai_deta~t {c |}{res}   0.7536   1.0000

{txt}
{com}. *
. *
. *
. 
. 
. 
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. * compute interquartile range difference (change from 25th percentile values to 75th percentile values) for above within-state measures [FULL SAMPLE] *
. 
. * w_sai_detamt_clmtreal_0.25 -->  w_sai_detamt_clmtreal_0.75 = $3,017,598 
. display 590808 -  -2426790
{res}3017598
{txt}
{com}. 
. 
. 
. * compute interquartile range difference (change from 25th percentile values to 75th percentile values) for above within-state measures [RESTRICTED SAMPLE] *
. 
. *w_sai_detamt_clmtreal_omit_0.25 -->  w_sai_detamt_clmtreal_omit_0.75 = $3,173,463.5
. display  590394.5 - -2583069 
{res}3173463.5
{txt}
{com}. 
. 
. 
. 
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. 
. *** COMPUTE WITHIN-STATE DESCRIPTIVE STATISTICS SINCE MODEL ESTIMATES ARE WITHIN-STATE EFFECTS [DIRECT GUBERNATORIAL APPOINTMENT REGIME SUBSAMPLE] *** 
. 
. 
. 
. * compute descriptive statistics & correlations for overall measure and within-state measures for each dependent variable [DIRECT GUBERNATORIAL APPOINTMENT REGIME SUBSAMPLE] *
.  
. sum sai_detamt_clmtreal w_sai_detamt_clmtreal w_sai_detamt_clmtreal_omit if gubapptauth_partisan_rescaled4==1, detail

                     {txt}sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res} 265529.8       137599.1
{txt} 5%    {res} 429138.2       139155.7
{txt}10%    {res} 584054.2       147937.9       {txt}Obs         {res}        827
{txt}25%    {res}  1470565       172050.5       {txt}Sum of wgt. {res}        827

{txt}50%    {res}  4007998                      {txt}Mean          {res} 1.21e+07
                        {txt}Largest       Std. dev.     {res} 2.92e+07
{txt}75%    {res} 1.05e+07       2.05e+08
{txt}90%    {res} 3.12e+07       2.39e+08       {txt}Variance      {res} 8.54e+14
{txt}95%    {res} 4.89e+07       2.45e+08       {txt}Skewness      {res} 7.846756
{txt}99%    {res} 1.34e+08       4.74e+08       {txt}Kurtosis      {res} 94.49023

                    {txt}w_sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res}-4.12e+07      -5.81e+07
{txt} 5%    {res}-1.89e+07      -5.71e+07
{txt}10%    {res} -8690952      -5.59e+07       {txt}Obs         {res}        827
{txt}25%    {res} -2658287      -5.45e+07       {txt}Sum of wgt. {res}        827

{txt}50%    {res}-561798.1                      {txt}Mean          {res} 114036.6
                        {txt}Largest       Std. dev.     {res} 2.22e+07
{txt}75%    {res} 614364.2       1.60e+08
{txt}90%    {res}  3756580       1.61e+08       {txt}Variance      {res} 4.94e+14
{txt}95%    {res} 1.23e+07       1.85e+08       {txt}Skewness      {res} 9.207728
{txt}99%    {res} 8.17e+07       3.96e+08       {txt}Kurtosis      {res} 140.0443

                 {txt}w_sai_detamt_clmtreal_omit
{hline 61}
      Percentiles      Smallest
 1%    {res}-4.12e+07      -5.81e+07
{txt} 5%    {res}-1.89e+07      -5.71e+07
{txt}10%    {res} -8690952      -5.59e+07       {txt}Obs         {res}        827
{txt}25%    {res} -2607617      -5.45e+07       {txt}Sum of wgt. {res}        827

{txt}50%    {res}-540052.5                      {txt}Mean          {res} 113437.3
                        {txt}Largest       Std. dev.     {res} 2.22e+07
{txt}75%    {res} 614751.3       1.60e+08
{txt}90%    {res}  3756580       1.61e+08       {txt}Variance      {res} 4.94e+14
{txt}95%    {res} 1.23e+07       1.85e+08       {txt}Skewness      {res} 9.207815
{txt}99%    {res} 8.17e+07       3.96e+08       {txt}Kurtosis      {res} 140.0484
{txt}
{com}. 
. * Set Global Macro of IQR
. sum w_sai_detamt_clmtreal if gubapptauth_partisan_rescaled4==1, detail

                    {txt}w_sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res}-4.12e+07      -5.81e+07
{txt} 5%    {res}-1.89e+07      -5.71e+07
{txt}10%    {res} -8690952      -5.59e+07       {txt}Obs         {res}        827
{txt}25%    {res} -2658287      -5.45e+07       {txt}Sum of wgt. {res}        827

{txt}50%    {res}-561798.1                      {txt}Mean          {res} 114036.6
                        {txt}Largest       Std. dev.     {res} 2.22e+07
{txt}75%    {res} 614364.2       1.60e+08
{txt}90%    {res}  3756580       1.61e+08       {txt}Variance      {res} 4.94e+14
{txt}95%    {res} 1.23e+07       1.85e+08       {txt}Skewness      {res} 9.207728
{txt}99%    {res} 8.17e+07       3.96e+08       {txt}Kurtosis      {res} 140.0443
{txt}
{com}. global amtiqr_m1m3_rescaled4_1 = round(r(p75),1)-round(r(p25),1)
{txt}
{com}. di $amtiqr_m1m3_rescaled4_1
{res}3272651
{txt}
{com}. 
. sum w_sai_detamt_clmtreal_omit if gubapptauth_partisan_rescaled4==1, detail

                 {txt}w_sai_detamt_clmtreal_omit
{hline 61}
      Percentiles      Smallest
 1%    {res}-4.12e+07      -5.81e+07
{txt} 5%    {res}-1.89e+07      -5.71e+07
{txt}10%    {res} -8690952      -5.59e+07       {txt}Obs         {res}        827
{txt}25%    {res} -2607617      -5.45e+07       {txt}Sum of wgt. {res}        827

{txt}50%    {res}-540052.5                      {txt}Mean          {res} 113437.3
                        {txt}Largest       Std. dev.     {res} 2.22e+07
{txt}75%    {res} 614751.3       1.60e+08
{txt}90%    {res}  3756580       1.61e+08       {txt}Variance      {res} 4.94e+14
{txt}95%    {res} 1.23e+07       1.85e+08       {txt}Skewness      {res} 9.207815
{txt}99%    {res} 8.17e+07       3.96e+08       {txt}Kurtosis      {res} 140.0484
{txt}
{com}. global amtiqr_m2m4_rescaled4_1 = round(r(p75),1)-round(r(p25),1)
{txt}
{com}. di $amtiqr_m2m4_rescaled4_1
{res}3222368
{txt}
{com}. *
. *
. *
. *
. *
. 
. 
. * compute interquartile range difference (change from 25th percentile values to 75th percentile values) for above within-state measures [DIRECT GUBERNATORIAL APPOINTMENT REGIME SUBSAMPLE] *
. 
. * w_sai_detamt_clmtreal_0.25 -->  w_sai_detamt_clmtreal_0.75 = $3,272,651.2 [Full Model of Observations] * 
. di $amtiqr_m1m3_rescaled4_1
{res}3272651
{txt}
{com}. display 614364.2 - -2658287 
{res}3272651.2
{txt}
{com}. *
. *
. * w_sai_detamt_clmtreal_omit_0.25 -->  w_sai_detamt_clmtreal_omit_0.75 = $3,222,368.3 [Restricted Model of Observations] *  
. di $amtiqr_m2m4_rescaled4_1
{res}3222368
{txt}
{com}. display   614751.3  -  -2607617
{res}3222368.3
{txt}
{com}. 
. 
. 
. 
. 
. 
. **** 0. Generate Descriptive Statistics Table [full sample of observations]
. cd "C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Output Files"
{res}C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Output Files
{txt}
{com}. 
. estpost summarize sai_detamt_clmtreal  gubapptauth_partisan_rescaled4  gubapptauth_partisan_rescaled3   electionyear econideol_median  publicunion_cov  unemp_rate  ln_uiadmin_budget_real ln_pop_size ln_emp_contributions ln_clmterror_est ln_employerappeals_ct if !missing(clmterror_est)

{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(count)}{space 1}{space 1}{ralign 9:e(sum_w)}{space 1}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(Var)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(min)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:sai_detamt~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.20e+07}}}{space 1}{space 1}{ralign 9:{res:{sf: 8.17e+14}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.86e+07}}}{space 1}{space 1}{ralign 9:{res:{sf: 247.3008}}}{space 1}
{space 0}{space 0}{ralign 12:gubapptaut~4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf: .8278278}}}{space 1}{space 1}{ralign 9:{res:{sf: .1426717}}}{space 1}{space 1}{ralign 9:{res:{sf: .3777191}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:gubapptaut~3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:  1.23023}}}{space 1}{space 1}{ralign 9:{res:{sf: .5220912}}}{space 1}{space 1}{ralign 9:{res:{sf: .7225588}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:electionyear}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf: .2632633}}}{space 1}{space 1}{ralign 9:{res:{sf: .1941501}}}{space 1}{space 1}{ralign 9:{res:{sf: .4406246}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}
{space 0}{space 0}{ralign 12:econideol_~n}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:-.0237316}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.534957}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.238934}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.470595}}}{space 1}
{space 0}{space 0}{ralign 12:publicunio~v}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf: 36.58428}}}{space 1}{space 1}{ralign 9:{res:{sf: 312.3261}}}{space 1}{space 1}{ralign 9:{res:{sf: 17.67275}}}{space 1}{space 1}{ralign 9:{res:{sf:      4.3}}}{space 1}
{space 0}{space 0}{ralign 12:unemp_rate}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.669828}}}{space 1}{space 1}{ralign 9:{res:{sf:  4.15126}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.037464}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.108333}}}{space 1}
{space 0}{space 0}{ralign 12:ln_uiadmin~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf: 17.47025}}}{space 1}{space 1}{ralign 9:{res:{sf: .8830342}}}{space 1}{space 1}{ralign 9:{res:{sf:  .939699}}}{space 1}{space 1}{ralign 9:{res:{sf: 15.43047}}}{space 1}
{space 0}{space 0}{ralign 12:ln_pop_size}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf: 14.13577}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.382837}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.175941}}}{space 1}{space 1}{ralign 9:{res:{sf: 10.71837}}}{space 1}
{space 0}{space 0}{ralign 12:ln_emp_con~s}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf: 11.45514}}}{space 1}{space 1}{ralign 9:{res:{sf: .8881978}}}{space 1}{space 1}{ralign 9:{res:{sf: .9424425}}}{space 1}{space 1}{ralign 9:{res:{sf: 9.698981}}}{space 1}
{space 0}{space 0}{ralign 12:ln_clmterr~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:  17.5283}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.022677}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.422209}}}{space 1}{space 1}{ralign 9:{res:{sf: 13.49347}}}{space 1}
{space 0}{space 0}{ralign 12:ln_employe~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf:      999}}}{space 1}{space 1}{ralign 9:{res:{sf: 8.154962}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.799361}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.341403}}}{space 1}{space 1}{ralign 9:{res:{sf:  4.59512}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:e(max)}{space 1}{space 1}{ralign 9:e(sum)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:sai_detamt~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 4.74e+08}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.19e+10}}}{space 1}
{space 0}{space 0}{ralign 12:gubapptaut~4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      827}}}{space 1}
{space 0}{space 0}{ralign 12:gubapptaut~3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf:     1229}}}{space 1}
{space 0}{space 0}{ralign 12:electionyear}{space 1}{c |}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:      263}}}{space 1}
{space 0}{space 0}{ralign 12:econideol_~n}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 3.325148}}}{space 1}{space 1}{ralign 9:{res:{sf:-23.70783}}}{space 1}
{space 0}{space 0}{ralign 12:publicunio~v}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     76.2}}}{space 1}{space 1}{ralign 9:{res:{sf:  36547.7}}}{space 1}
{space 0}{space 0}{ralign 12:unemp_rate}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 13.78333}}}{space 1}{space 1}{ralign 9:{res:{sf: 5664.158}}}{space 1}
{space 0}{space 0}{ralign 12:ln_uiadmin~l}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  20.3258}}}{space 1}{space 1}{ralign 9:{res:{sf: 17452.78}}}{space 1}
{space 0}{space 0}{ralign 12:ln_pop_size}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 17.87561}}}{space 1}{space 1}{ralign 9:{res:{sf: 14121.63}}}{space 1}
{space 0}{space 0}{ralign 12:ln_emp_con~s}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 14.22346}}}{space 1}{space 1}{ralign 9:{res:{sf: 11443.69}}}{space 1}
{space 0}{space 0}{ralign 12:ln_clmterr~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  22.2016}}}{space 1}{space 1}{ralign 9:{res:{sf: 17510.77}}}{space 1}
{space 0}{space 0}{ralign 12:ln_employe~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 10.93528}}}{space 1}{space 1}{ralign 9:{res:{sf: 8146.807}}}{space 1}

{com}. 
. esttab using TableA2.02-01-2026.rtf, cells("count mean sd min max") noobs replace
{res}{txt}(output written to {browse  `"TableA2.02-01-2026.rtf"'})

{com}. 
. 
. 
. ************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. * Generate Per Case Variable 
. 
. 
. egen b_sai_detct_clmt = mean(sai_detct_clmt), by(state_numident)
{txt}
{com}. gen w_sai_detct_clmt = sai_detct_clmt - b_sai_detct_clmt
{txt}
{com}. 
. sum sai_detct_clmt w_sai_detct_clmt, detail

                       {txt}sai_detct_clmt
{hline 61}
      Percentiles      Smallest
 1%    {res}      758             32
{txt} 5%    {res}     1379            106
{txt}10%    {res}     1859            483       {txt}Obs         {res}        999
{txt}25%    {res}     4004            549       {txt}Sum of wgt. {res}        999

{txt}50%    {res}     8447                      {txt}Mean          {res} 17806.89
                        {txt}Largest       Std. dev.     {res} 29287.77
{txt}75%    {res}    16893         239836
{txt}90%    {res}    45897         245960       {txt}Variance      {res} 8.58e+08
{txt}95%    {res}    70580         281237       {txt}Skewness      {res} 4.673654
{txt}99%    {res}   136438         341210       {txt}Kurtosis      {res} 35.16882

                      {txt}w_sai_detct_clmt
{hline 61}
      Percentiles      Smallest
 1%    {res} -41794.6       -78394.6
{txt} 5%    {res} -14741.5      -77519.15
{txt}10%    {res}-7396.449       -74845.6       {txt}Obs         {res}        999
{txt}25%    {res}  -2867.4       -70909.6       {txt}Sum of wgt. {res}        999

{txt}50%    {res}-597.6016                      {txt}Mean          {res} 5.87e-06
                        {txt}Largest       Std. dev.     {res} 16994.72
{txt}75%    {res}  1602.85       122045.4
{txt}90%    {res}  5966.35       128169.4       {txt}Variance      {res} 2.89e+08
{txt}95%    {res}  13276.8       182377.8       {txt}Skewness      {res} 6.835434
{txt}99%    {res} 56544.45       281660.4       {txt}Kurtosis      {res} 101.3329
{txt}
{com}. 
. 
. gen sai_clmterror_percase=sai_detamt_clmtreal/sai_detct_clmt
{txt}
{com}. sum sai_clmterror_percase, detail

                    {txt}sai_clmterror_percase
{hline 61}
      Percentiles      Smallest
 1%    {res} 96.51524       7.728149
{txt} 5%    {res} 203.1655       80.86504
{txt}10%    {res} 248.2691       86.42022       {txt}Obs         {res}        999
{txt}25%    {res} 326.1949        88.4986       {txt}Sum of wgt. {res}        999

{txt}50%    {res} 473.3318                      {txt}Mean          {res} 568.7343
                        {txt}Largest       Std. dev.     {res}  422.667
{txt}75%    {res} 666.3358       3396.609
{txt}90%    {res} 961.1882       3780.698       {txt}Variance      {res} 178647.4
{txt}95%    {res} 1211.551       3997.872       {txt}Skewness      {res} 3.825713
{txt}99%    {res} 2171.776       4912.155       {txt}Kurtosis      {res} 27.26243
{txt}
{com}. 
. 
. 
. *
. *
. egen b_sai_detct_clmt_omit = mean(sai_detct_clmt) if nonpartisan_gubapprove!=1, by(state_numident)
{txt}(47 missing values generated)

{com}. gen w_sai_detct_clmt_omit = sai_detct_clmt - b_sai_detct_clmt_omit
{txt}(47 missing values generated)

{com}. 
. * Compute interquartile range difference for within-state claimant error detection total counts [Full Sample]
. sum w_sai_detct_clmt if gubapptauth_partisan_rescaled4==1, detail

                      {txt}w_sai_detct_clmt
{hline 61}
      Percentiles      Smallest
 1%    {res}-36173.58       -78394.6
{txt} 5%    {res} -14741.5       -74845.6
{txt}10%    {res} -7321.65       -70909.6       {txt}Obs         {res}        827
{txt}25%    {res} -2637.65       -60514.6       {txt}Sum of wgt. {res}        827

{txt}50%    {res}  -519.05                      {txt}Mean          {res} 160.4878
                        {txt}Largest       Std. dev.     {res} 16722.24
{txt}75%    {res}  1639.85       117320.3
{txt}90%    {res}   6052.4       122045.4       {txt}Variance      {res} 2.80e+08
{txt}95%    {res} 13084.85       128169.4       {txt}Skewness      {res} 7.256267
{txt}99%    {res} 56544.45       281660.4       {txt}Kurtosis      {res} 112.2767
{txt}
{com}. 
. global ctiqr_m1m3_rescaled4_1 = round(r(p75)-r(p25),1)
{txt}
{com}. di $ctiqr_m1m3_rescaled4_1
{res}4278
{txt}
{com}. di round(r(p75)-r(p25), 0.01)
{res}4277.5
{txt}
{com}. * = 4,277.50
. 
. * Compute interquartile range difference for within-state claimant error detection total counts [Restricted Sample]
. sum w_sai_detct_clmt_omit if gubapptauth_partisan_rescaled4==1 & nonpartisan_gubapprove!=1, detail

                    {txt}w_sai_detct_clmt_omit
{hline 61}
      Percentiles      Smallest
 1%    {res}-36173.58       -78394.6
{txt} 5%    {res} -14741.5       -74845.6
{txt}10%    {res} -7321.65       -70909.6       {txt}Obs         {res}        827
{txt}25%    {res} -2670.05       -60514.6       {txt}Sum of wgt. {res}        827

{txt}50%    {res}-523.1992                      {txt}Mean          {res} 148.5945
                        {txt}Largest       Std. dev.     {res} 16722.21
{txt}75%    {res}  1639.85       117320.3
{txt}90%    {res}   6052.4       122045.4       {txt}Variance      {res} 2.80e+08
{txt}95%    {res} 13084.85       128169.4       {txt}Skewness      {res} 7.258104
{txt}99%    {res} 56544.45       281660.4       {txt}Kurtosis      {res} 112.2978
{txt}
{com}. 
. global ctiqr_m2m4_rescaled4_1 = round(r(p75)-r(p25),1)
{txt}
{com}. di $ctiqr_m2m4_rescaled4_1
{res}4310
{txt}
{com}. di round(r(p75)-r(p25), 1)
{res}4310
{txt}
{com}. * = 4,309.9
. *
. *
. * Compute interquartile range difference for within-state claimant error detection total counts [Full Sample]
. sum w_sai_detct_clmt if gubapptauth_partisan_rescaled3==1, detail

                      {txt}w_sai_detct_clmt
{hline 61}
      Percentiles      Smallest
 1%    {res}-40551.58       -74845.6
{txt} 5%    {res} -18467.4       -70909.6
{txt}10%    {res}  -8715.9       -60514.6       {txt}Obs         {res}        425
{txt}25%    {res}  -3671.5       -41794.6       {txt}Sum of wgt. {res}        425

{txt}50%    {res}-627.2002                      {txt}Mean          {res}-407.1673
                        {txt}Largest       Std. dev.     {res}  18879.2
{txt}75%    {res}  1491.85        73380.4
{txt}90%    {res}  4761.55        75591.6       {txt}Variance      {res} 3.56e+08
{txt}95%    {res}  12318.8       79263.95       {txt}Skewness      {res} 8.054958
{txt}99%    {res} 59223.95       281660.4       {txt}Kurtosis      {res} 121.9024
{txt}
{com}. 
. global ctiqr_m1m3_rescaled3_1 = round(r(p75)-r(p25), 1)
{txt}
{com}. di $ctiqr_m1m3_rescaled3_1
{res}5163
{txt}
{com}. di round(r(p75)-r(p25), 0.01)
{res}5163.35
{txt}
{com}. * = 5,163.35
. 
. sum w_sai_detct_clmt if gubapptauth_partisan_rescaled3==2, detail

                      {txt}w_sai_detct_clmt
{hline 61}
      Percentiles      Smallest
 1%    {res} -23224.7       -78394.6
{txt} 5%    {res}  -9833.4       -42518.6
{txt}10%    {res}  -6512.6       -26387.7       {txt}Obs         {res}        402
{txt}25%    {res} -2099.15      -24465.45       {txt}Sum of wgt. {res}        402

{txt}50%    {res}-457.4749                      {txt}Mean          {res} 760.6206
                        {txt}Largest       Std. dev.     {res} 14086.62
{txt}75%    {res}   1843.5       56544.45
{txt}90%    {res} 6161.297       117320.3       {txt}Variance      {res} 1.98e+08
{txt}95%    {res} 14481.85       122045.4       {txt}Skewness      {res} 4.617805
{txt}99%    {res}  44076.3       128169.4       {txt}Kurtosis      {res} 46.44909
{txt}
{com}. 
. global ctiqr_m1m3_rescaled3_2 = round(r(p75)-r(p25), 1)
{txt}
{com}. di $ctiqr_m1m3_rescaled3_2
{res}3943
{txt}
{com}. di round(r(p75)-r(p25), 0.01)
{res}3942.65
{txt}
{com}. * = 3,942.65
. 
. sum w_sai_detct_clmt if gubapptauth_partisan_rescaled3==1|gubapptauth_partisan_rescaled3==2, detail

                      {txt}w_sai_detct_clmt
{hline 61}
      Percentiles      Smallest
 1%    {res}-36173.58       -78394.6
{txt} 5%    {res} -14741.5       -74845.6
{txt}10%    {res} -7321.65       -70909.6       {txt}Obs         {res}        827
{txt}25%    {res} -2637.65       -60514.6       {txt}Sum of wgt. {res}        827

{txt}50%    {res}  -519.05                      {txt}Mean          {res} 160.4878
                        {txt}Largest       Std. dev.     {res} 16722.24
{txt}75%    {res}  1639.85       117320.3
{txt}90%    {res}   6052.4       122045.4       {txt}Variance      {res} 2.80e+08
{txt}95%    {res} 13084.85       128169.4       {txt}Skewness      {res} 7.256267
{txt}99%    {res} 56544.45       281660.4       {txt}Kurtosis      {res} 112.2767
{txt}
{com}. 
. global ctiqr_m1m3_rescaled3_1or2 = round(r(p75)-r(p25), 1)
{txt}
{com}. di $ctiqr_m1m3_rescaled3_1or2
{res}4278
{txt}
{com}. di round(r(p75)-r(p25),  1)
{res}4278
{txt}
{com}. * = 4,277.5
. 
. 
. 
. 
. 
. * Compute interquartile range difference for within-state claimant error detection total counts [Restricted Sample]
. sum w_sai_detct_clmt_omit if gubapptauth_partisan_rescaled3==1 & nonpartisan_gubapprove!=1, detail

                    {txt}w_sai_detct_clmt_omit
{hline 61}
      Percentiles      Smallest
 1%    {res}-40551.58       -74845.6
{txt} 5%    {res} -18467.4       -70909.6
{txt}10%    {res}  -8715.9       -60514.6       {txt}Obs         {res}        425
{txt}25%    {res}  -3671.5       -41794.6       {txt}Sum of wgt. {res}        425

{txt}50%    {res}-627.2002                      {txt}Mean          {res}-416.6938
                        {txt}Largest       Std. dev.     {res} 18878.92
{txt}75%    {res}  1491.85        73380.4
{txt}90%    {res}  4761.55        75591.6       {txt}Variance      {res} 3.56e+08
{txt}95%    {res} 11967.84       79263.95       {txt}Skewness      {res} 8.056581
{txt}99%    {res} 59223.95       281660.4       {txt}Kurtosis      {res} 121.9255
{txt}
{com}. 
. global ctiqr_m2m4_rescaled3_1 = round(r(p75)-r(p25), 1)
{txt}
{com}. di $ctiqr_m2m4_rescaled3_1
{res}5163
{txt}
{com}. di round(r(p75)-r(p25),  1)
{res}5163
{txt}
{com}. * = 5,163.35
. 
. sum w_sai_detct_clmt_omit if gubapptauth_partisan_rescaled3==2 & nonpartisan_gubapprove!=1, detail

                    {txt}w_sai_detct_clmt_omit
{hline 61}
      Percentiles      Smallest
 1%    {res} -23224.7       -78394.6
{txt} 5%    {res}  -9833.4       -42518.6
{txt}10%    {res}  -6512.6       -26387.7       {txt}Obs         {res}        402
{txt}25%    {res}-2164.526      -24465.45       {txt}Sum of wgt. {res}        402

{txt}50%    {res}-457.4749                      {txt}Mean          {res} 746.2251
                        {txt}Largest       Std. dev.     {res} 14087.15
{txt}75%    {res}   1843.5       56544.45
{txt}90%    {res} 6161.297       117320.3       {txt}Variance      {res} 1.98e+08
{txt}95%    {res} 14481.85       122045.4       {txt}Skewness      {res} 4.619808
{txt}99%    {res}  44076.3       128169.4       {txt}Kurtosis      {res} 46.46039
{txt}
{com}. 
. global ctiqr_m2m4_rescaled3_2 = round(r(p75)-r(p25), 1)
{txt}
{com}. di $ctiqr_m2m4_rescaled3_2
{res}4008
{txt}
{com}. di round(r(p75)-r(p25),  1)
{res}4008
{txt}
{com}. * = 4,008.03
. 
. sum w_sai_detct_clmt_omit if gubapptauth_partisan_rescaled3==1 & nonpartisan_gubapprove!=1|gubapptauth_partisan_rescaled3==2 & nonpartisan_gubapprove!=1, detail

                    {txt}w_sai_detct_clmt_omit
{hline 61}
      Percentiles      Smallest
 1%    {res}-36173.58       -78394.6
{txt} 5%    {res} -14741.5       -74845.6
{txt}10%    {res} -7321.65       -70909.6       {txt}Obs         {res}        827
{txt}25%    {res} -2670.05       -60514.6       {txt}Sum of wgt. {res}        827

{txt}50%    {res}-523.1992                      {txt}Mean          {res} 148.5945
                        {txt}Largest       Std. dev.     {res} 16722.21
{txt}75%    {res}  1639.85       117320.3
{txt}90%    {res}   6052.4       122045.4       {txt}Variance      {res} 2.80e+08
{txt}95%    {res} 13084.85       128169.4       {txt}Skewness      {res} 7.258104
{txt}99%    {res} 56544.45       281660.4       {txt}Kurtosis      {res} 112.2978
{txt}
{com}. 
. global ctiqr_m2m4_rescaled3_1or2 = round(r(p75)-r(p25), 1)
{txt}
{com}. di $ctiqr_m2m4_rescaled3_1or2
{res}4310
{txt}
{com}. di round(r(p75)-r(p25),  1)
{res}4310
{txt}
{com}. * =4,309.9
. 
. 
. 
. 
. 
. **** POLITICAL INEQUALITY BIAS IN ADMINISTRATIVE ERROR DETECTION TARGETING CLAIMANT OVERPAYMENTS: DISTINCTIONS BETWEEN NON-PARTISAN AND PARTISAN DIRECT GUBERNATORIAL APPOINTMENT AUTHORITY  **** 
. 
. 
. 
. 
. 
. 
. 
. *** SAVE UPDATED STATISTICAL DATABASE WITH VARIBALES INCORPORATED FROM ABOVE PROGRAM CODE as of 07-05-2025 [THIS DATABASE IS USED IN APPENDIX STATISTICAL ANALYSES] *** 
. 
. 
. save "C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\Data Replication File Materials (JPART)\Data\Admin_Bias.MANUSCRIPT DATABASE.07-07-2025.dta", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\Data Replication File Materials (JPART)\Data\Admin_Bias.MANUSCRIPT DATABASE.07-07-2025.dta{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\Data Replication File Materials (JPART)\Data\Admin_Bias.MANUSCRIPT DATABASE.07-07-2025.dta{rm}
saved
{p_end}

{com}. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. *** NOTE: PERFORM PAIRWISE REGRESSIONS ON SAME GUBERNATORIAL APPOINTMENT AUTHORITY MEASURE BETWEEN "DETAMT_CLMTREAL" DEPENDENT VARIABLES [FULL SAMPLE OF OBSERVATIONS &  RESTRICTED SAMPLE OF OBSERVATIONS OMIT OBSERVATIONS CONTAINED IN BASELINE NON-GUBERNATORIAL APPOINTMENT AUTHORITY CATEGORY [APPROVAL, BUT NO DIRECT GUBERNATORIAL APPOINTMENT AUTHORITY (N = 42) & NON-PARTISAN GOVERNOR OBSERVATIONS (N = 5)] [I.E., MODELS 1 & 2 / MODELS 3 & 4] ***
. 
. 
. 
. 
. 
. 
. 
. 
. *** MODELS 1 & 2:BENEFIT OVERPAYMENT ERROR DETECTION BY STATE UIP AGENCY-INITIATION [CLAIMANT-/NON-FRAUD] -- AMOUNTS IN 2010 CONSTANT-DOLLAR TERMS --- DISTINCTIONS BETWEEN GUBERNATORIAL APPOINTMENT AUTHORITY: NON-PARTISAN DISTINCTION *  
. 
. 
. 
. *** NON-PARTISAN BASELINE MODELS [MODELS 1 & 2]: DISTINCTION BETWEEN GUBERNATORIAL DIRECT APPOINTMENT POWERS VERSUS ABSENCE OF SUCH INSTITUTIONAL POWERS [BINARY INDICATOR MEASURE] ***
. *** gubapptauth_partisan_rescaled4==0 [ABSENCE OF DIRECT GUBERNATORIAL APPOINTMENT POWERS] &  gubapptauth_partisan_rescaled4==1 [DIRECT GUBERNATORIAL APPOINTMENT POWERS] ***
. 
. 
. 
. 
. 
. *** ESTIMATION OF MODELS 1 & 2 *** 
. 
. 
. ** MODEL 1 **
. 
. glm  sai_detamt_clmtreal i.gubapptauth_partisan_rescaled4  electionyear econideol_median  publicunion_cov  unemp_rate  ln_uiadmin_budget_real ln_pop_size  ln_clmterror_est ln_employerappeals_ct    i.state_numident i.year,  family(normal) link(log) vce(cluster state_numident)
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-18147.917}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-17827.871}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-17603.109}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-17598.784}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-17598.723}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-17598.722}  
{res}
{txt}Generalized linear models{col 51}Number of obs{col 67}={col 69}{res}       999
{txt}Optimization     : {res}ML{txt}{col 51}Residual df{col 67}={col 69}{res}       965
{col 20}{txt}{col 51}Scale parameter{col 67}={col 70}{res} 1.27e+14
{txt}Deviance{col 18}={res}{col 20} 1.17071e+17{txt}{col 51}(1/df) Deviance{col 67}={res}{col 70} 1.21e+14
{txt}Pearson{col 18}={res}{col 20} 1.17071e+17{txt}{col 51}(1/df) Pearson{col 67}={res}{col 70} 1.21e+14

{txt}Variance function: {res}V(u) = {col 27}1{col 51}{txt}[{res}Gaussian{txt}]
Link function    : {res}g(u) = {col 27}ln(u){col 51}{txt}[{res}Log{txt}]

{col 51}{help j_glmic##|_new:AIC}{col 67}={res}{col 70} 35.30075
{txt}Log pseudolikelihood{col 22}= {res}-17598.72248{txt}{col 51}{help j_glmic##|_new:BIC}{col 67}={res}{col 70} 1.17e+17

{txt}{ralign 84:(Std. err. adjusted for {res:50} clusters in {res:state_numident})}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}sai_detamt_clmtr~l{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
1.gubapptauth_pa~4 {c |}{col 20}{res}{space 2} .1158387{col 32}{space 2} .0899228{col 43}{space 1}    1.29{col 52}{space 3}0.198{col 60}{space 4}-.0604067{col 73}{space 3}  .292084
{txt}{space 6}electionyear {c |}{col 20}{res}{space 2} .1380543{col 32}{space 2} .1298181{col 43}{space 1}    1.06{col 52}{space 3}0.288{col 60}{space 4}-.1163845{col 73}{space 3}  .392493
{txt}{space 2}econideol_median {c |}{col 20}{res}{space 2}  1.10524{col 32}{space 2}  .280476{col 43}{space 1}    3.94{col 52}{space 3}0.000{col 60}{space 4} .5555167{col 73}{space 3} 1.654962
{txt}{space 3}publicunion_cov {c |}{col 20}{res}{space 2}-.0396967{col 32}{space 2} .0357619{col 43}{space 1}   -1.11{col 52}{space 3}0.267{col 60}{space 4}-.1097888{col 73}{space 3} .0303954
{txt}{space 8}unemp_rate {c |}{col 20}{res}{space 2}-.1496042{col 32}{space 2} .0301082{col 43}{space 1}   -4.97{col 52}{space 3}0.000{col 60}{space 4}-.2086151{col 73}{space 3}-.0905932
{txt}ln_uiadmin_budge~l {c |}{col 20}{res}{space 2} 1.915813{col 32}{space 2} .7863812{col 43}{space 1}    2.44{col 52}{space 3}0.015{col 60}{space 4} .3745345{col 73}{space 3} 3.457092
{txt}{space 7}ln_pop_size {c |}{col 20}{res}{space 2}-.2884037{col 32}{space 2} .5098466{col 43}{space 1}   -0.57{col 52}{space 3}0.572{col 60}{space 4}-1.287685{col 73}{space 3} .7108774
{txt}{space 2}ln_clmterror_est {c |}{col 20}{res}{space 2}  .205321{col 32}{space 2}  .133774{col 43}{space 1}    1.53{col 52}{space 3}0.125{col 60}{space 4}-.0568712{col 73}{space 3} .4675132
{txt}ln_employerappea~t {c |}{col 20}{res}{space 2}-.2019723{col 32}{space 2} .1912454{col 43}{space 1}   -1.06{col 52}{space 3}0.291{col 60}{space 4}-.5768064{col 73}{space 3} .1728617
{txt}{space 18} {c |}
{space 4}state_numident {c |}
{space 16}2  {c |}{col 20}{res}{space 2}-1.453889{col 32}{space 2} .4807702{col 43}{space 1}   -3.02{col 52}{space 3}0.002{col 60}{space 4}-2.396181{col 73}{space 3}-.5115964
{txt}{space 16}3  {c |}{col 20}{res}{space 2}-.8333506{col 32}{space 2} .3337171{col 43}{space 1}   -2.50{col 52}{space 3}0.013{col 60}{space 4}-1.487424{col 73}{space 3}-.1792772
{txt}{space 16}4  {c |}{col 20}{res}{space 2}-1.429922{col 32}{space 2} .5358278{col 43}{space 1}   -2.67{col 52}{space 3}0.008{col 60}{space 4}-2.480126{col 73}{space 3}-.3797191
{txt}{space 16}5  {c |}{col 20}{res}{space 2}-7.702164{col 32}{space 2} 2.248116{col 43}{space 1}   -3.43{col 52}{space 3}0.001{col 60}{space 4}-12.10839{col 73}{space 3}-3.295937
{txt}{space 16}6  {c |}{col 20}{res}{space 2}-1.840881{col 32}{space 2} .6201801{col 43}{space 1}   -2.97{col 52}{space 3}0.003{col 60}{space 4}-3.056412{col 73}{space 3}-.6253504
{txt}{space 16}7  {c |}{col 20}{res}{space 2}-5.508248{col 32}{space 2} 1.493846{col 43}{space 1}   -3.69{col 52}{space 3}0.000{col 60}{space 4}-8.436132{col 73}{space 3}-2.580365
{txt}{space 16}8  {c |}{col 20}{res}{space 2} -2.34792{col 32}{space 2} .7613121{col 43}{space 1}   -3.08{col 52}{space 3}0.002{col 60}{space 4}-3.840065{col 73}{space 3} -.855776
{txt}{space 16}9  {c |}{col 20}{res}{space 2}  .004285{col 32}{space 2} .6337061{col 43}{space 1}    0.01{col 52}{space 3}0.995{col 60}{space 4}-1.237756{col 73}{space 3} 1.246326
{txt}{space 15}10  {c |}{col 20}{res}{space 2}-1.831059{col 32}{space 2} .7181842{col 43}{space 1}   -2.55{col 52}{space 3}0.011{col 60}{space 4}-3.238675{col 73}{space 3}-.4234442
{txt}{space 15}11  {c |}{col 20}{res}{space 2}-2.752613{col 32}{space 2} .7811109{col 43}{space 1}   -3.52{col 52}{space 3}0.000{col 60}{space 4}-4.283562{col 73}{space 3}-1.221664
{txt}{space 15}12  {c |}{col 20}{res}{space 2}-.8643286{col 32}{space 2} .3987467{col 43}{space 1}   -2.17{col 52}{space 3}0.030{col 60}{space 4}-1.645858{col 73}{space 3}-.0827993
{txt}{space 15}13  {c |}{col 20}{res}{space 2}-2.304549{col 32}{space 2} 1.215914{col 43}{space 1}   -1.90{col 52}{space 3}0.058{col 60}{space 4}-4.687697{col 73}{space 3} .0785994
{txt}{space 15}14  {c |}{col 20}{res}{space 2} -.633488{col 32}{space 2} .3819817{col 43}{space 1}   -1.66{col 52}{space 3}0.097{col 60}{space 4}-1.382158{col 73}{space 3} .1151824
{txt}{space 15}15  {c |}{col 20}{res}{space 2}-1.892291{col 32}{space 2} .3753961{col 43}{space 1}   -5.04{col 52}{space 3}0.000{col 60}{space 4}-2.628054{col 73}{space 3}-1.156528
{txt}{space 15}16  {c |}{col 20}{res}{space 2}-.8350223{col 32}{space 2}  .258286{col 43}{space 1}   -3.23{col 52}{space 3}0.001{col 60}{space 4}-1.341253{col 73}{space 3} -.328791
{txt}{space 15}17  {c |}{col 20}{res}{space 2}-1.186545{col 32}{space 2} .3991853{col 43}{space 1}   -2.97{col 52}{space 3}0.003{col 60}{space 4}-1.968934{col 73}{space 3}-.4041567
{txt}{space 15}18  {c |}{col 20}{res}{space 2}-.7380314{col 32}{space 2} .6623708{col 43}{space 1}   -1.11{col 52}{space 3}0.265{col 60}{space 4}-2.036254{col 73}{space 3} .5601915
{txt}{space 15}19  {c |}{col 20}{res}{space 2}-3.393378{col 32}{space 2} .8934154{col 43}{space 1}   -3.80{col 52}{space 3}0.000{col 60}{space 4} -5.14444{col 73}{space 3}-1.642316
{txt}{space 15}20  {c |}{col 20}{res}{space 2}-5.486201{col 32}{space 2} 1.405877{col 43}{space 1}   -3.90{col 52}{space 3}0.000{col 60}{space 4} -8.24167{col 73}{space 3}-2.730732
{txt}{space 15}21  {c |}{col 20}{res}{space 2} -4.72308{col 32}{space 2} 1.277534{col 43}{space 1}   -3.70{col 52}{space 3}0.000{col 60}{space 4}-7.227001{col 73}{space 3}-2.219158
{txt}{space 15}22  {c |}{col 20}{res}{space 2}-1.046599{col 32}{space 2} 1.090938{col 43}{space 1}   -0.96{col 52}{space 3}0.337{col 60}{space 4}-3.184798{col 73}{space 3} 1.091599
{txt}{space 15}23  {c |}{col 20}{res}{space 2}-1.418515{col 32}{space 2} 1.003122{col 43}{space 1}   -1.41{col 52}{space 3}0.157{col 60}{space 4}-3.384598{col 73}{space 3} .5475675
{txt}{space 15}24  {c |}{col 20}{res}{space 2} .4327243{col 32}{space 2} .5748002{col 43}{space 1}    0.75{col 52}{space 3}0.452{col 60}{space 4}-.6938634{col 73}{space 3} 1.559312
{txt}{space 15}25  {c |}{col 20}{res}{space 2}-1.209911{col 32}{space 2} .4816653{col 43}{space 1}   -2.51{col 52}{space 3}0.012{col 60}{space 4}-2.153958{col 73}{space 3}-.2658645
{txt}{space 15}26  {c |}{col 20}{res}{space 2} -1.97775{col 32}{space 2} .5391746{col 43}{space 1}   -3.67{col 52}{space 3}0.000{col 60}{space 4}-3.034513{col 73}{space 3}-.9209871
{txt}{space 15}27  {c |}{col 20}{res}{space 2}-2.404469{col 32}{space 2} .6310271{col 43}{space 1}   -3.81{col 52}{space 3}0.000{col 60}{space 4}-3.641259{col 73}{space 3}-1.167679
{txt}{space 15}28  {c |}{col 20}{res}{space 2}  1.46981{col 32}{space 2} .6259573{col 43}{space 1}    2.35{col 52}{space 3}0.019{col 60}{space 4} .2429566{col 73}{space 3} 2.696664
{txt}{space 15}29  {c |}{col 20}{res}{space 2}-2.643268{col 32}{space 2} .5985536{col 43}{space 1}   -4.42{col 52}{space 3}0.000{col 60}{space 4}-3.816412{col 73}{space 3}-1.470125
{txt}{space 15}30  {c |}{col 20}{res}{space 2}-1.891838{col 32}{space 2} 1.369524{col 43}{space 1}   -1.38{col 52}{space 3}0.167{col 60}{space 4}-4.576056{col 73}{space 3} .7923796
{txt}{space 15}31  {c |}{col 20}{res}{space 2}-2.168915{col 32}{space 2} .7703798{col 43}{space 1}   -2.82{col 52}{space 3}0.005{col 60}{space 4}-3.678832{col 73}{space 3}-.6589983
{txt}{space 15}32  {c |}{col 20}{res}{space 2}-6.529862{col 32}{space 2} 1.916187{col 43}{space 1}   -3.41{col 52}{space 3}0.001{col 60}{space 4}-10.28552{col 73}{space 3}-2.774204
{txt}{space 15}33  {c |}{col 20}{res}{space 2} -1.80092{col 32}{space 2} 1.038695{col 43}{space 1}   -1.73{col 52}{space 3}0.083{col 60}{space 4}-3.836726{col 73}{space 3}  .234885
{txt}{space 15}34  {c |}{col 20}{res}{space 2}-.5793946{col 32}{space 2}   .80355{col 43}{space 1}   -0.72{col 52}{space 3}0.471{col 60}{space 4}-2.154324{col 73}{space 3} .9955345
{txt}{space 15}35  {c |}{col 20}{res}{space 2}-2.151512{col 32}{space 2} .9605106{col 43}{space 1}   -2.24{col 52}{space 3}0.025{col 60}{space 4}-4.034078{col 73}{space 3}-.2689461
{txt}{space 15}36  {c |}{col 20}{res}{space 2}-2.066749{col 32}{space 2} .4726011{col 43}{space 1}   -4.37{col 52}{space 3}0.000{col 60}{space 4} -2.99303{col 73}{space 3}-1.140468
{txt}{space 15}37  {c |}{col 20}{res}{space 2}-5.646265{col 32}{space 2} 1.292883{col 43}{space 1}   -4.37{col 52}{space 3}0.000{col 60}{space 4}-8.180268{col 73}{space 3}-3.112262
{txt}{space 15}38  {c |}{col 20}{res}{space 2}-2.544505{col 32}{space 2} 1.264024{col 43}{space 1}   -2.01{col 52}{space 3}0.044{col 60}{space 4}-5.021947{col 73}{space 3}-.0670626
{txt}{space 15}39  {c |}{col 20}{res}{space 2}-2.365386{col 32}{space 2} 1.298817{col 43}{space 1}   -1.82{col 52}{space 3}0.069{col 60}{space 4}-4.911021{col 73}{space 3} .1802486
{txt}{space 15}40  {c |}{col 20}{res}{space 2}-.7542481{col 32}{space 2} .7978789{col 43}{space 1}   -0.95{col 52}{space 3}0.344{col 60}{space 4}-2.318062{col 73}{space 3} .8095657
{txt}{space 15}41  {c |}{col 20}{res}{space 2}-1.395872{col 32}{space 2} .8234455{col 43}{space 1}   -1.70{col 52}{space 3}0.090{col 60}{space 4}-3.009796{col 73}{space 3} .2180512
{txt}{space 15}42  {c |}{col 20}{res}{space 2}-1.282288{col 32}{space 2} .2985162{col 43}{space 1}   -4.30{col 52}{space 3}0.000{col 60}{space 4}-1.867369{col 73}{space 3}-.6972066
{txt}{space 15}43  {c |}{col 20}{res}{space 2} -.590672{col 32}{space 2} 1.033474{col 43}{space 1}   -0.57{col 52}{space 3}0.568{col 60}{space 4}-2.616245{col 73}{space 3} 1.434901
{txt}{space 15}44  {c |}{col 20}{res}{space 2}-1.108632{col 32}{space 2} .3950657{col 43}{space 1}   -2.81{col 52}{space 3}0.005{col 60}{space 4}-1.882947{col 73}{space 3}-.3343174
{txt}{space 15}45  {c |}{col 20}{res}{space 2}-4.506205{col 32}{space 2}  .922402{col 43}{space 1}   -4.89{col 52}{space 3}0.000{col 60}{space 4} -6.31408{col 73}{space 3}-2.698331
{txt}{space 15}46  {c |}{col 20}{res}{space 2}-2.981152{col 32}{space 2} .9304767{col 43}{space 1}   -3.20{col 52}{space 3}0.001{col 60}{space 4}-4.804853{col 73}{space 3}-1.157452
{txt}{space 15}47  {c |}{col 20}{res}{space 2}-.6040369{col 32}{space 2} 1.043702{col 43}{space 1}   -0.58{col 52}{space 3}0.563{col 60}{space 4}-2.649654{col 73}{space 3} 1.441581
{txt}{space 15}48  {c |}{col 20}{res}{space 2}-.6547562{col 32}{space 2} .4165332{col 43}{space 1}   -1.57{col 52}{space 3}0.116{col 60}{space 4}-1.471146{col 73}{space 3} .1616338
{txt}{space 15}49  {c |}{col 20}{res}{space 2}-2.571854{col 32}{space 2} .7927231{col 43}{space 1}   -3.24{col 52}{space 3}0.001{col 60}{space 4}-4.125563{col 73}{space 3}-1.018145
{txt}{space 15}50  {c |}{col 20}{res}{space 2}-.5067315{col 32}{space 2} .7238087{col 43}{space 1}   -0.70{col 52}{space 3}0.484{col 60}{space 4} -1.92537{col 73}{space 3} .9119074
{txt}{space 18} {c |}
{space 14}year {c |}
{space 13}2003  {c |}{col 20}{res}{space 2} .4635995{col 32}{space 2} .3141867{col 43}{space 1}    1.48{col 52}{space 3}0.140{col 60}{space 4}-.1521952{col 73}{space 3} 1.079394
{txt}{space 13}2004  {c |}{col 20}{res}{space 2} .4635133{col 32}{space 2} .4951987{col 43}{space 1}    0.94{col 52}{space 3}0.349{col 60}{space 4}-.5070583{col 73}{space 3} 1.434085
{txt}{space 13}2005  {c |}{col 20}{res}{space 2} .0606292{col 32}{space 2} .6607414{col 43}{space 1}    0.09{col 52}{space 3}0.927{col 60}{space 4}  -1.2344{col 73}{space 3} 1.355659
{txt}{space 13}2006  {c |}{col 20}{res}{space 2}-.0767015{col 32}{space 2} .7097392{col 43}{space 1}   -0.11{col 52}{space 3}0.914{col 60}{space 4}-1.467765{col 73}{space 3} 1.314362
{txt}{space 13}2007  {c |}{col 20}{res}{space 2} .1620875{col 32}{space 2} .7141385{col 43}{space 1}    0.23{col 52}{space 3}0.820{col 60}{space 4}-1.237598{col 73}{space 3} 1.561773
{txt}{space 13}2008  {c |}{col 20}{res}{space 2} .4477649{col 32}{space 2} .7162497{col 43}{space 1}    0.63{col 52}{space 3}0.532{col 60}{space 4}-.9560587{col 73}{space 3} 1.851589
{txt}{space 13}2009  {c |}{col 20}{res}{space 2} 1.626402{col 32}{space 2} .2490738{col 43}{space 1}    6.53{col 52}{space 3}0.000{col 60}{space 4} 1.138226{col 73}{space 3} 2.114577
{txt}{space 13}2010  {c |}{col 20}{res}{space 2} 1.931824{col 32}{space 2}  .254718{col 43}{space 1}    7.58{col 52}{space 3}0.000{col 60}{space 4} 1.432586{col 73}{space 3} 2.431062
{txt}{space 13}2011  {c |}{col 20}{res}{space 2} 1.812413{col 32}{space 2} .3338526{col 43}{space 1}    5.43{col 52}{space 3}0.000{col 60}{space 4} 1.158074{col 73}{space 3} 2.466752
{txt}{space 13}2012  {c |}{col 20}{res}{space 2} 1.429797{col 32}{space 2}  .367122{col 43}{space 1}    3.89{col 52}{space 3}0.000{col 60}{space 4} .7102514{col 73}{space 3} 2.149343
{txt}{space 13}2013  {c |}{col 20}{res}{space 2}  1.59194{col 32}{space 2} .3548143{col 43}{space 1}    4.49{col 52}{space 3}0.000{col 60}{space 4} .8965163{col 73}{space 3} 2.287363
{txt}{space 13}2014  {c |}{col 20}{res}{space 2} 1.055104{col 32}{space 2}   .43379{col 43}{space 1}    2.43{col 52}{space 3}0.015{col 60}{space 4} .2048912{col 73}{space 3} 1.905317
{txt}{space 13}2015  {c |}{col 20}{res}{space 2} .7356762{col 32}{space 2} .7362451{col 43}{space 1}    1.00{col 52}{space 3}0.318{col 60}{space 4}-.7073377{col 73}{space 3}  2.17869
{txt}{space 13}2016  {c |}{col 20}{res}{space 2}  .316813{col 32}{space 2} .7976332{col 43}{space 1}    0.40{col 52}{space 3}0.691{col 60}{space 4}-1.246519{col 73}{space 3} 1.880145
{txt}{space 13}2017  {c |}{col 20}{res}{space 2} .3570431{col 32}{space 2} .8076944{col 43}{space 1}    0.44{col 52}{space 3}0.658{col 60}{space 4}-1.226009{col 73}{space 3} 1.940095
{txt}{space 13}2018  {c |}{col 20}{res}{space 2}  .356807{col 32}{space 2} .8342286{col 43}{space 1}    0.43{col 52}{space 3}0.669{col 60}{space 4}-1.278251{col 73}{space 3} 1.991865
{txt}{space 13}2019  {c |}{col 20}{res}{space 2}-.0522725{col 32}{space 2} .9165506{col 43}{space 1}   -0.06{col 52}{space 3}0.955{col 60}{space 4}-1.848679{col 73}{space 3} 1.744134
{txt}{space 13}2020  {c |}{col 20}{res}{space 2} 2.353007{col 32}{space 2} .4177685{col 43}{space 1}    5.63{col 52}{space 3}0.000{col 60}{space 4} 1.534196{col 73}{space 3} 3.171818
{txt}{space 13}2021  {c |}{col 20}{res}{space 2} 1.569134{col 32}{space 2}  .668681{col 43}{space 1}    2.35{col 52}{space 3}0.019{col 60}{space 4} .2585431{col 73}{space 3} 2.879724
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}-13.17638{col 32}{space 2} 10.07065{col 43}{space 1}   -1.31{col 52}{space 3}0.191{col 60}{space 4} -32.9145{col 73}{space 3} 6.561737
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       999{col 28}        .{col 39}-17598.72{col 50}    34{col 58} 35265.44{col 69} 35432.27
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. *
. estimate store m1
{txt}
{com}. *
. *
. *
. 
. 
. ** MODEL 2 **
. 
. glm  sai_detamt_clmtreal  i.gubapptauth_partisan_rescaled4  electionyear    econideol_median  publicunion_cov  unemp_rate  ln_uiadmin_budget_real ln_pop_size  ln_clmterror_est  ln_employerappeals_ct  i.state_numident i.year if nonpartisan_gubapprove!=1,  family(normal) link(log) vce(cluster state_numident)
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-17310.522}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-17007.493}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-16796.852}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-16792.732}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-16792.669}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-16792.668}  
{res}
{txt}Generalized linear models{col 51}Number of obs{col 67}={col 69}{res}       952
{txt}Optimization     : {res}ML{txt}{col 51}Residual df{col 67}={col 69}{res}       918
{col 20}{txt}{col 51}Scale parameter{col 67}={col 70}{res} 1.33e+14
{txt}Deviance{col 18}={res}{col 20} 1.16819e+17{txt}{col 51}(1/df) Deviance{col 67}={res}{col 70} 1.27e+14
{txt}Pearson{col 18}={res}{col 20} 1.16819e+17{txt}{col 51}(1/df) Pearson{col 67}={res}{col 70} 1.27e+14

{txt}Variance function: {res}V(u) = {col 27}1{col 51}{txt}[{res}Gaussian{txt}]
Link function    : {res}g(u) = {col 27}ln(u){col 51}{txt}[{res}Log{txt}]

{col 51}{help j_glmic##|_new:AIC}{col 67}={res}{col 70} 35.35014
{txt}Log pseudolikelihood{col 22}= {res}-16792.66786{txt}{col 51}{help j_glmic##|_new:BIC}{col 67}={res}{col 70} 1.17e+17

{txt}{ralign 84:(Std. err. adjusted for {res:48} clusters in {res:state_numident})}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}sai_detamt_clmtr~l{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
1.gubapptauth_pa~4 {c |}{col 20}{res}{space 2} .1246635{col 32}{space 2} .0891253{col 43}{space 1}    1.40{col 52}{space 3}0.162{col 60}{space 4} -.050019{col 73}{space 3} .2993459
{txt}{space 6}electionyear {c |}{col 20}{res}{space 2} .1381591{col 32}{space 2} .1299771{col 43}{space 1}    1.06{col 52}{space 3}0.288{col 60}{space 4}-.1165914{col 73}{space 3} .3929097
{txt}{space 2}econideol_median {c |}{col 20}{res}{space 2} 1.107421{col 32}{space 2} .2801745{col 43}{space 1}    3.95{col 52}{space 3}0.000{col 60}{space 4} .5582893{col 73}{space 3} 1.656553
{txt}{space 3}publicunion_cov {c |}{col 20}{res}{space 2}-.0398877{col 32}{space 2} .0358285{col 43}{space 1}   -1.11{col 52}{space 3}0.266{col 60}{space 4}-.1101103{col 73}{space 3}  .030335
{txt}{space 8}unemp_rate {c |}{col 20}{res}{space 2}-.1506697{col 32}{space 2} .0303619{col 43}{space 1}   -4.96{col 52}{space 3}0.000{col 60}{space 4}-.2101779{col 73}{space 3}-.0911614
{txt}ln_uiadmin_budge~l {c |}{col 20}{res}{space 2} 1.917108{col 32}{space 2} .7872663{col 43}{space 1}    2.44{col 52}{space 3}0.015{col 60}{space 4} .3740939{col 73}{space 3} 3.460121
{txt}{space 7}ln_pop_size {c |}{col 20}{res}{space 2}-.2906054{col 32}{space 2} .5114382{col 43}{space 1}   -0.57{col 52}{space 3}0.570{col 60}{space 4}-1.293006{col 73}{space 3}  .711795
{txt}{space 2}ln_clmterror_est {c |}{col 20}{res}{space 2} .2072112{col 32}{space 2} .1343939{col 43}{space 1}    1.54{col 52}{space 3}0.123{col 60}{space 4} -.056196{col 73}{space 3} .4706184
{txt}ln_employerappea~t {c |}{col 20}{res}{space 2}-.2012916{col 32}{space 2} .1912529{col 43}{space 1}   -1.05{col 52}{space 3}0.293{col 60}{space 4}-.5761403{col 73}{space 3} .1735571
{txt}{space 18} {c |}
{space 4}state_numident {c |}
{space 16}3  {c |}{col 20}{res}{space 2}-.8303486{col 32}{space 2}  .334832{col 43}{space 1}   -2.48{col 52}{space 3}0.013{col 60}{space 4}-1.486607{col 73}{space 3}  -.17409
{txt}{space 16}4  {c |}{col 20}{res}{space 2} -1.43817{col 32}{space 2} .5353403{col 43}{space 1}   -2.69{col 52}{space 3}0.007{col 60}{space 4}-2.487418{col 73}{space 3}-.3889224
{txt}{space 16}5  {c |}{col 20}{res}{space 2}-7.712549{col 32}{space 2} 2.251864{col 43}{space 1}   -3.42{col 52}{space 3}0.001{col 60}{space 4}-12.12612{col 73}{space 3}-3.298976
{txt}{space 16}6  {c |}{col 20}{res}{space 2}-1.852282{col 32}{space 2}  .620686{col 43}{space 1}   -2.98{col 52}{space 3}0.003{col 60}{space 4}-3.068804{col 73}{space 3}-.6357594
{txt}{space 16}7  {c |}{col 20}{res}{space 2}  -5.5132{col 32}{space 2} 1.497913{col 43}{space 1}   -3.68{col 52}{space 3}0.000{col 60}{space 4}-8.449056{col 73}{space 3}-2.577343
{txt}{space 16}8  {c |}{col 20}{res}{space 2}-2.353632{col 32}{space 2} .7619537{col 43}{space 1}   -3.09{col 52}{space 3}0.002{col 60}{space 4}-3.847034{col 73}{space 3}-.8602301
{txt}{space 16}9  {c |}{col 20}{res}{space 2}-.0016404{col 32}{space 2} .6369876{col 43}{space 1}   -0.00{col 52}{space 3}0.998{col 60}{space 4}-1.250113{col 73}{space 3} 1.246832
{txt}{space 15}10  {c |}{col 20}{res}{space 2}-1.827185{col 32}{space 2} .7184028{col 43}{space 1}   -2.54{col 52}{space 3}0.011{col 60}{space 4}-3.235229{col 73}{space 3}-.4191414
{txt}{space 15}11  {c |}{col 20}{res}{space 2}-2.754665{col 32}{space 2} .7857993{col 43}{space 1}   -3.51{col 52}{space 3}0.000{col 60}{space 4}-4.294803{col 73}{space 3}-1.214527
{txt}{space 15}12  {c |}{col 20}{res}{space 2}-.8690977{col 32}{space 2}  .397774{col 43}{space 1}   -2.18{col 52}{space 3}0.029{col 60}{space 4} -1.64872{col 73}{space 3} -.089475
{txt}{space 15}13  {c |}{col 20}{res}{space 2} -2.31023{col 32}{space 2} 1.219986{col 43}{space 1}   -1.89{col 52}{space 3}0.058{col 60}{space 4}-4.701359{col 73}{space 3} .0808994
{txt}{space 15}14  {c |}{col 20}{res}{space 2}-.6408119{col 32}{space 2} .3843669{col 43}{space 1}   -1.67{col 52}{space 3}0.095{col 60}{space 4}-1.394157{col 73}{space 3} .1125334
{txt}{space 15}15  {c |}{col 20}{res}{space 2}-1.901847{col 32}{space 2} .3768596{col 43}{space 1}   -5.05{col 52}{space 3}0.000{col 60}{space 4}-2.640478{col 73}{space 3}-1.163216
{txt}{space 15}16  {c |}{col 20}{res}{space 2} -.843073{col 32}{space 2} .2588413{col 43}{space 1}   -3.26{col 52}{space 3}0.001{col 60}{space 4}-1.350393{col 73}{space 3}-.3357533
{txt}{space 15}18  {c |}{col 20}{res}{space 2} -.743381{col 32}{space 2} .6618727{col 43}{space 1}   -1.12{col 52}{space 3}0.261{col 60}{space 4}-2.040628{col 73}{space 3} .5538657
{txt}{space 15}19  {c |}{col 20}{res}{space 2}-3.402344{col 32}{space 2} .8943552{col 43}{space 1}   -3.80{col 52}{space 3}0.000{col 60}{space 4}-5.155248{col 73}{space 3} -1.64944
{txt}{space 15}20  {c |}{col 20}{res}{space 2}-5.497324{col 32}{space 2} 1.406598{col 43}{space 1}   -3.91{col 52}{space 3}0.000{col 60}{space 4}-8.254206{col 73}{space 3}-2.740442
{txt}{space 15}21  {c |}{col 20}{res}{space 2}-4.761552{col 32}{space 2} 1.278243{col 43}{space 1}   -3.73{col 52}{space 3}0.000{col 60}{space 4}-7.266864{col 73}{space 3}-2.256241
{txt}{space 15}22  {c |}{col 20}{res}{space 2}-1.047454{col 32}{space 2} 1.095259{col 43}{space 1}   -0.96{col 52}{space 3}0.339{col 60}{space 4}-3.194122{col 73}{space 3} 1.099214
{txt}{space 15}23  {c |}{col 20}{res}{space 2}-1.427598{col 32}{space 2}   1.0085{col 43}{space 1}   -1.42{col 52}{space 3}0.157{col 60}{space 4}-3.404222{col 73}{space 3} .5490258
{txt}{space 15}24  {c |}{col 20}{res}{space 2} .4310772{col 32}{space 2} .5741212{col 43}{space 1}    0.75{col 52}{space 3}0.453{col 60}{space 4}-.6941797{col 73}{space 3} 1.556334
{txt}{space 15}25  {c |}{col 20}{res}{space 2}-1.216051{col 32}{space 2} .4854361{col 43}{space 1}   -2.51{col 52}{space 3}0.012{col 60}{space 4}-2.167488{col 73}{space 3}-.2646136
{txt}{space 15}26  {c |}{col 20}{res}{space 2}-1.981966{col 32}{space 2} .5419187{col 43}{space 1}   -3.66{col 52}{space 3}0.000{col 60}{space 4}-3.044107{col 73}{space 3}-.9198243
{txt}{space 15}27  {c |}{col 20}{res}{space 2}-2.415518{col 32}{space 2} .6265042{col 43}{space 1}   -3.86{col 52}{space 3}0.000{col 60}{space 4}-3.643443{col 73}{space 3}-1.187592
{txt}{space 15}28  {c |}{col 20}{res}{space 2} 1.469692{col 32}{space 2} .6327343{col 43}{space 1}    2.32{col 52}{space 3}0.020{col 60}{space 4}  .229556{col 73}{space 3} 2.709829
{txt}{space 15}29  {c |}{col 20}{res}{space 2}-2.646455{col 32}{space 2} .6024192{col 43}{space 1}   -4.39{col 52}{space 3}0.000{col 60}{space 4}-3.827174{col 73}{space 3}-1.465735
{txt}{space 15}30  {c |}{col 20}{res}{space 2}-1.896972{col 32}{space 2} 1.374439{col 43}{space 1}   -1.38{col 52}{space 3}0.168{col 60}{space 4}-4.590823{col 73}{space 3} .7968793
{txt}{space 15}31  {c |}{col 20}{res}{space 2}    -2.18{col 32}{space 2} .7685996{col 43}{space 1}   -2.84{col 52}{space 3}0.005{col 60}{space 4}-3.686428{col 73}{space 3}-.6735727
{txt}{space 15}32  {c |}{col 20}{res}{space 2}-6.536551{col 32}{space 2} 1.920761{col 43}{space 1}   -3.40{col 52}{space 3}0.001{col 60}{space 4}-10.30117{col 73}{space 3}-2.771929
{txt}{space 15}33  {c |}{col 20}{res}{space 2}-1.811221{col 32}{space 2} 1.038409{col 43}{space 1}   -1.74{col 52}{space 3}0.081{col 60}{space 4}-3.846464{col 73}{space 3} .2240228
{txt}{space 15}34  {c |}{col 20}{res}{space 2}-.5866146{col 32}{space 2} .8007275{col 43}{space 1}   -0.73{col 52}{space 3}0.464{col 60}{space 4}-2.156012{col 73}{space 3} .9827824
{txt}{space 15}35  {c |}{col 20}{res}{space 2}-2.156562{col 32}{space 2} .9642463{col 43}{space 1}   -2.24{col 52}{space 3}0.025{col 60}{space 4} -4.04645{col 73}{space 3}-.2666742
{txt}{space 15}36  {c |}{col 20}{res}{space 2}-2.065641{col 32}{space 2}  .472615{col 43}{space 1}   -4.37{col 52}{space 3}0.000{col 60}{space 4} -2.99195{col 73}{space 3}-1.139333
{txt}{space 15}37  {c |}{col 20}{res}{space 2}-5.653502{col 32}{space 2} 1.295812{col 43}{space 1}   -4.36{col 52}{space 3}0.000{col 60}{space 4}-8.193246{col 73}{space 3}-3.113757
{txt}{space 15}38  {c |}{col 20}{res}{space 2}-2.550466{col 32}{space 2} 1.267915{col 43}{space 1}   -2.01{col 52}{space 3}0.044{col 60}{space 4}-5.035534{col 73}{space 3} -.065398
{txt}{space 15}39  {c |}{col 20}{res}{space 2}-2.505705{col 32}{space 2} 1.283879{col 43}{space 1}   -1.95{col 52}{space 3}0.051{col 60}{space 4}-5.022062{col 73}{space 3} .0106508
{txt}{space 15}40  {c |}{col 20}{res}{space 2} -.761889{col 32}{space 2} .7980829{col 43}{space 1}   -0.95{col 52}{space 3}0.340{col 60}{space 4}-2.326103{col 73}{space 3} .8023247
{txt}{space 15}41  {c |}{col 20}{res}{space 2}-1.401587{col 32}{space 2} .8202389{col 43}{space 1}   -1.71{col 52}{space 3}0.087{col 60}{space 4}-3.009225{col 73}{space 3} .2060522
{txt}{space 15}42  {c |}{col 20}{res}{space 2}-1.290039{col 32}{space 2} .2987845{col 43}{space 1}   -4.32{col 52}{space 3}0.000{col 60}{space 4}-1.875646{col 73}{space 3}-.7044321
{txt}{space 15}43  {c |}{col 20}{res}{space 2}-.5913385{col 32}{space 2}  1.03477{col 43}{space 1}   -0.57{col 52}{space 3}0.568{col 60}{space 4} -2.61945{col 73}{space 3} 1.436773
{txt}{space 15}44  {c |}{col 20}{res}{space 2} -1.11635{col 32}{space 2}  .393213{col 43}{space 1}   -2.84{col 52}{space 3}0.005{col 60}{space 4}-1.887033{col 73}{space 3}-.3456667
{txt}{space 15}45  {c |}{col 20}{res}{space 2}-4.516451{col 32}{space 2}  .921028{col 43}{space 1}   -4.90{col 52}{space 3}0.000{col 60}{space 4}-6.321633{col 73}{space 3}-2.711269
{txt}{space 15}46  {c |}{col 20}{res}{space 2}-2.994572{col 32}{space 2} .9308159{col 43}{space 1}   -3.22{col 52}{space 3}0.001{col 60}{space 4}-4.818938{col 73}{space 3}-1.170206
{txt}{space 15}47  {c |}{col 20}{res}{space 2}-.6091609{col 32}{space 2} 1.047468{col 43}{space 1}   -0.58{col 52}{space 3}0.561{col 60}{space 4}-2.662161{col 73}{space 3} 1.443839
{txt}{space 15}48  {c |}{col 20}{res}{space 2}-.6584972{col 32}{space 2} .4197981{col 43}{space 1}   -1.57{col 52}{space 3}0.117{col 60}{space 4}-1.481286{col 73}{space 3} .1642918
{txt}{space 15}49  {c |}{col 20}{res}{space 2} -2.58078{col 32}{space 2} .7952203{col 43}{space 1}   -3.25{col 52}{space 3}0.001{col 60}{space 4}-4.139383{col 73}{space 3}-1.022177
{txt}{space 15}50  {c |}{col 20}{res}{space 2}-.5125921{col 32}{space 2} .7206457{col 43}{space 1}   -0.71{col 52}{space 3}0.477{col 60}{space 4}-1.925032{col 73}{space 3} .8998475
{txt}{space 18} {c |}
{space 14}year {c |}
{space 13}2003  {c |}{col 20}{res}{space 2} .4677146{col 32}{space 2} .3245231{col 43}{space 1}    1.44{col 52}{space 3}0.150{col 60}{space 4} -.168339{col 73}{space 3} 1.103768
{txt}{space 13}2004  {c |}{col 20}{res}{space 2} .4676725{col 32}{space 2} .5068506{col 43}{space 1}    0.92{col 52}{space 3}0.356{col 60}{space 4}-.5257364{col 73}{space 3} 1.461081
{txt}{space 13}2005  {c |}{col 20}{res}{space 2} .0631572{col 32}{space 2} .6723382{col 43}{space 1}    0.09{col 52}{space 3}0.925{col 60}{space 4}-1.254602{col 73}{space 3} 1.380916
{txt}{space 13}2006  {c |}{col 20}{res}{space 2}-.0738058{col 32}{space 2} .7204289{col 43}{space 1}   -0.10{col 52}{space 3}0.918{col 60}{space 4} -1.48582{col 73}{space 3} 1.338209
{txt}{space 13}2007  {c |}{col 20}{res}{space 2} .1656569{col 32}{space 2} .7248019{col 43}{space 1}    0.23{col 52}{space 3}0.819{col 60}{space 4}-1.254929{col 73}{space 3} 1.586242
{txt}{space 13}2008  {c |}{col 20}{res}{space 2} .4516375{col 32}{space 2} .7274864{col 43}{space 1}    0.62{col 52}{space 3}0.535{col 60}{space 4}-.9742096{col 73}{space 3} 1.877485
{txt}{space 13}2009  {c |}{col 20}{res}{space 2}   1.6343{col 32}{space 2} .2586316{col 43}{space 1}    6.32{col 52}{space 3}0.000{col 60}{space 4} 1.127391{col 73}{space 3} 2.141209
{txt}{space 13}2010  {c |}{col 20}{res}{space 2} 1.940346{col 32}{space 2} .2634171{col 43}{space 1}    7.37{col 52}{space 3}0.000{col 60}{space 4} 1.424058{col 73}{space 3} 2.456635
{txt}{space 13}2011  {c |}{col 20}{res}{space 2} 1.821543{col 32}{space 2} .3446816{col 43}{space 1}    5.28{col 52}{space 3}0.000{col 60}{space 4} 1.145979{col 73}{space 3} 2.497106
{txt}{space 13}2012  {c |}{col 20}{res}{space 2} 1.436586{col 32}{space 2} .3753999{col 43}{space 1}    3.83{col 52}{space 3}0.000{col 60}{space 4} .7008159{col 73}{space 3} 2.172356
{txt}{space 13}2013  {c |}{col 20}{res}{space 2} 1.598844{col 32}{space 2} .3632775{col 43}{space 1}    4.40{col 52}{space 3}0.000{col 60}{space 4} .8868329{col 73}{space 3} 2.310855
{txt}{space 13}2014  {c |}{col 20}{res}{space 2} 1.059912{col 32}{space 2} .4424769{col 43}{space 1}    2.40{col 52}{space 3}0.017{col 60}{space 4} .1926732{col 73}{space 3} 1.927151
{txt}{space 13}2015  {c |}{col 20}{res}{space 2} .7399076{col 32}{space 2} .7472424{col 43}{space 1}    0.99{col 52}{space 3}0.322{col 60}{space 4}-.7246606{col 73}{space 3} 2.204476
{txt}{space 13}2016  {c |}{col 20}{res}{space 2}  .319556{col 32}{space 2} .8105277{col 43}{space 1}    0.39{col 52}{space 3}0.693{col 60}{space 4}-1.269049{col 73}{space 3} 1.908161
{txt}{space 13}2017  {c |}{col 20}{res}{space 2} .3585726{col 32}{space 2}  .821013{col 43}{space 1}    0.44{col 52}{space 3}0.662{col 60}{space 4}-1.250583{col 73}{space 3} 1.967728
{txt}{space 13}2018  {c |}{col 20}{res}{space 2} .3583025{col 32}{space 2} .8475104{col 43}{space 1}    0.42{col 52}{space 3}0.672{col 60}{space 4}-1.302787{col 73}{space 3} 2.019392
{txt}{space 13}2019  {c |}{col 20}{res}{space 2} -.051653{col 32}{space 2} .9292668{col 43}{space 1}   -0.06{col 52}{space 3}0.956{col 60}{space 4}-1.872982{col 73}{space 3} 1.769677
{txt}{space 13}2020  {c |}{col 20}{res}{space 2} 2.361643{col 32}{space 2}  .425745{col 43}{space 1}    5.55{col 52}{space 3}0.000{col 60}{space 4} 1.527198{col 73}{space 3} 3.196088
{txt}{space 13}2021  {c |}{col 20}{res}{space 2} 1.573484{col 32}{space 2} .6795318{col 43}{space 1}    2.32{col 52}{space 3}0.021{col 60}{space 4} .2416258{col 73}{space 3} 2.905341
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}-13.20256{col 32}{space 2} 10.07029{col 43}{space 1}   -1.31{col 52}{space 3}0.190{col 60}{space 4}-32.93997{col 73}{space 3} 6.534849
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       952{col 28}        .{col 39}-16792.67{col 50}    34{col 58} 33653.34{col 69} 33818.53
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. *
. estimate store m2
{txt}
{com}. *
. *
. *
. 
. estimates dir

{txt}{hline 13}{c TT}{hline 58}
             {c |}           Dependent  Number of        
        Name {c |} Command    variable     param.  Title 
{hline 13}{c +}{hline 58}
{ralign 12:{stata estimates replay m1:m1}}{col 14}{txt:{c |}}{res}{col 16}{lalign 10:glm}{col 26}{ralign 9:sai_detamt~l}{col 42}  81{col 48}{it:Generalized linear models}
{txt}{ralign 12:{stata estimates replay m2:m2}}{col 14}{txt:{c |}}{res}{col 16}{lalign 10:glm}{col 26}{ralign 9:sai_detamt~l}{col 42}  79{col 48}{it:Generalized linear models}
{txt}{hline 13}{c BT}{hline 58}

{com}. 
. 
. 
. 
. ***** WITHIN-OVERPAYMENT ERROR DETECTION HYPOTHESES ******
. 
. 
. 
. 
. ** FIGURE 2 [TOP PORTION: GOV. DIRECT APPOINTMENT (BINARY)]: BASELINE UNCONDITIONAL APPOINTMENT MARGINAL EFFECTS [GOVERNOR'S HAVE DIRECT APPOINTMENT AUTHORITY: NO PARTISAN DISTINCTIONS] ** 
. 
. macro list _all
{txt}{p 0 16}
GLIST:{space 10}{res}{res}SGLM_s1 SGLM_ph SGLM_running SGLM_lf SGLM_lt SGLM_mu SGLM_vf SGLM_vt SGLM_f SGLM_p SGLM_a SGLM_m SGLM_y SGLM_L SGLM_V
{p_end}
{txt}{p 0 16}
ctiqr_m2m4_rescaled3_1or2:{break}
{res}4310
{p_end}
{txt}{p 0 16}
ctiqr_m2m4_rescaled3_2:{break}
{res}4008
{p_end}
{txt}{p 0 16}
ctiqr_m2m4_rescaled3_1:{break}
{res}5163
{p_end}
{txt}{p 0 16}
ctiqr_m1m3_rescaled3_1or2:{break}
{res}4278
{p_end}
{txt}{p 0 16}
ctiqr_m1m3_rescaled3_2:{break}
{res}3943
{p_end}
{txt}{p 0 16}
ctiqr_m1m3_rescaled3_1:{break}
{res}5163
{p_end}
{txt}{p 0 16}
ctiqr_m2m4_rescaled4_1:{break}
{res}4310
{p_end}
{txt}{p 0 16}
ctiqr_m1m3_rescaled4_1:{break}
{res}4278
{p_end}
{txt}{p 0 16}
amtiqr_m2m4_rescaled4_1:{break}
{res}3222368
{p_end}
{txt}{p 0 16}
amtiqr_m1m3_rescaled4_1:{break}
{res}3272651
{p_end}
{txt}{p 0 16}
S_level:{space 8}{res}{res}95
{p_end}
{txt}{p 0 16}
F1:{space 13}{res}{res}help advice;
{p_end}
{txt}{p 0 16}
F2:{space 13}{res}{res}describe;
{p_end}
{txt}{p 0 16}
F7:{space 13}{res}{res}save 
{p_end}
{txt}{p 0 16}
F8:{space 13}{res}{res}use 
{p_end}
{txt}{p 0 16}
S_ADO:{space 10}{res}{res}BASE;SITE;.;PERSONAL;PLUS;OLDPLACE
{p_end}
{txt}{p 0 16}
S_StataMP:{space 6}{res}{res}MP
{p_end}
{txt}{p 0 16}
S_StataSE:{space 6}{res}{res}SE
{p_end}
{txt}{p 0 16}
S_OS:{space 11}{res}{res}Windows
{p_end}
{txt}{p 0 16}
S_OSDTL:{space 8}{res}{res}64-bit
{p_end}
{txt}{p 0 16}
S_MACH:{space 9}{res}{res}PC (64-bit x86-64)
{p_end}
{txt}{p 0 16}
S_FN:{space 11}{res}{res}C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\Data Replication File Materials (JPART)\Data\Admin_Bias.MANUSCRIPT DATABASE.07-07-2025.dta
{p_end}
{txt}{p 0 16}
S_FNDATE:{space 7}{res}{res} 1 Feb 2026 15:11
{p_end}
{txt}
{com}. 
. estimate restore m1
{txt}(results {stata estimates replay m1:m1} are active now)

{com}. lincom 1.gubapptauth_partisan_rescaled4*$amtiqr_m1m3_rescaled4_1

{p 0 7}{space 1}{text:( 1)}{space 1} {res}3272651{res}*{res}[sai_detamt_clmtreal]1.gubapptauth_partisan_rescaled4 = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}sai_detamt~l{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 379099.6{col 26}{space 2} 294285.8{col 37}{space 1}    1.29{col 46}{space 3}0.198{col 54}{space 4}  -197690{col 67}{space 3} 955889.1
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix m1a = 1, 1, 1, r(estimate), r(lb), r(ub), r(p)
{txt}
{com}. *
. di round(r(estimate)/$ctiqr_m1m3_rescaled4_1, 0.01)
{res}88.62
{txt}
{com}. *
. *
. *
. estimate restore m2
{txt}(results {stata estimates replay m2:m2} are active now)

{com}. lincom 1.gubapptauth_partisan_rescaled4*$amtiqr_m2m4_rescaled4_1

{p 0 7}{space 1}{text:( 1)}{space 1} {res}3222368{res}*{res}[sai_detamt_clmtreal]1.gubapptauth_partisan_rescaled4 = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}sai_detamt~l{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 401711.5{col 26}{space 2} 287194.6{col 37}{space 1}    1.40{col 46}{space 3}0.162{col 54}{space 4}-161179.6{col 67}{space 3} 964602.7
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix m1d = 2, 1, 2, r(estimate), r(lb), r(ub), r(p)
{txt}
{com}. *
. di round(r(estimate)/$ctiqr_m2m4_rescaled4_1, 0.01)
{res}93.2
{txt}
{com}. 
. *
. *
. *
. *
. *
. *
. *
. 
. 
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. 
. 
. 
. * compute descriptive statistics & correlations for overall measure and within-state measures for each dependent variable [broken down by each appointment regime] * 
.  
. 
. * non-direct gubernatorial appointment regime [gubapptauth_partisan_rescaled3==0] * 
. sum detamt_clmtreal  w_sai_detamt_clmtreal if gubapptauth_partisan_rescaled3==0, detail

               {txt}detamt_clmt in constant dollars
{hline 61}
      Percentiles      Smallest
 1%    {res} 80218.74       43973.57
{txt} 5%    {res} 984301.4       80218.74
{txt}10%    {res}  1415606       440361.2       {txt}Obs         {res}        172
{txt}25%    {res}  2283576       734109.6       {txt}Sum of wgt. {res}        172

{txt}50%    {res}  3484136                      {txt}Mean          {res} 1.13e+07
                        {txt}Largest       Std. dev.     {res} 2.53e+07
{txt}75%    {res}  6223559       6.94e+07
{txt}90%    {res} 2.87e+07       7.03e+07       {txt}Variance      {res} 6.42e+14
{txt}95%    {res} 5.78e+07       1.24e+08       {txt}Skewness      {res} 5.864279
{txt}99%    {res} 1.24e+08       2.49e+08       {txt}Kurtosis      {res} 49.08225

                    {txt}w_sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res}-3.84e+07      -5.12e+07
{txt} 5%    {res}-1.66e+07      -3.84e+07
{txt}10%    {res} -4179265      -3.66e+07       {txt}Obs         {res}        172
{txt}25%    {res} -2007190      -3.27e+07       {txt}Sum of wgt. {res}        172

{txt}50%    {res}-813358.8                      {txt}Mean          {res}  -548304
                        {txt}Largest       Std. dev.     {res} 1.76e+07
{txt}75%    {res} 489020.8       1.31e+07
{txt}90%    {res}  3182245       2.84e+07       {txt}Variance      {res} 3.09e+14
{txt}95%    {res}  6354786       6.40e+07       {txt}Skewness      {res} 7.328527
{txt}99%    {res} 6.40e+07       1.89e+08       {txt}Kurtosis      {res} 82.29826
{txt}
{com}. correlate detamt_clmtreal  w_sai_detamt_clmtreal if gubapptauth_partisan_rescaled3==0
{txt}(obs=172)

             {c |} de~treal w_sai_~l
{hline 13}{c +}{hline 18}
detamt_clm~l {c |}{res}   1.0000
{txt}w_sai_deta~l {c |}{res}   0.6734   1.0000

{txt}
{com}. *
. *
. *
. *
. 
. * compute interquartile range difference (change from 25th percentile values to 75th percentile values) for above within-state measures [FULL SAMPLE] * 
. 
. 
. * w_sai_detamt_clmtreal_0.25 -->  w_sai_detamt_clmtreal_0.75 = $2,496,210.8
. display 489020.8 - -2007190
{res}2496210.8
{txt}
{com}. 
. *
. *
. 
. * compute interquartile range difference (change from 25th percentile values to 75th percentile values) for above within-state measures [RESTRICTED SAMPLE] *  
. 
. * w_sai_detamt_clmtreal_omit_0.25 -->  w_sai_detamt_clmtreal_omit_0.75 = $2,934,238
. display 453859 - -2480379
{res}2934238
{txt}
{com}. 
. 
. 
. 
. 
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. 
. 
.  
. * Direct gubernatorial appointment regime [gubapptauth_partisan_rescaled3==1] * 
. sum sai_detamt_clmtreal  w_sai_detamt_clmtreal  w_sai_detamt_clmtreal_omit if gubapptauth_partisan_rescaled3==1, detail

                     {txt}sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res} 279850.9         239159
{txt} 5%    {res} 424139.5       247049.3
{txt}10%    {res} 566279.4       261998.5       {txt}Obs         {res}        425
{txt}25%    {res}  1267500       265529.8       {txt}Sum of wgt. {res}        425

{txt}50%    {res}  3257511                      {txt}Mean          {res}  9231249
                        {txt}Largest       Std. dev.     {res} 2.10e+07
{txt}75%    {res}  8237300       1.34e+08
{txt}90%    {res} 1.97e+07       1.72e+08       {txt}Variance      {res} 4.42e+14
{txt}95%    {res} 3.43e+07       1.90e+08       {txt}Skewness      {res} 5.855931
{txt}99%    {res} 1.32e+08       1.97e+08       {txt}Kurtosis      {res} 44.07996

                    {txt}w_sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res}-2.94e+07      -5.81e+07
{txt} 5%    {res}-1.63e+07      -5.59e+07
{txt}10%    {res} -8690952      -3.06e+07       {txt}Obs         {res}        425
{txt}25%    {res} -2390781      -3.01e+07       {txt}Sum of wgt. {res}        425

{txt}50%    {res}-418057.4                      {txt}Mean          {res}-276521.8
                        {txt}Largest       Std. dev.     {res} 1.40e+07
{txt}75%    {res} 625595.5       5.45e+07
{txt}90%    {res}  3864434       8.17e+07       {txt}Variance      {res} 1.97e+14
{txt}95%    {res}  9499331       9.90e+07       {txt}Skewness      {res} 4.947759
{txt}99%    {res} 5.37e+07       1.60e+08       {txt}Kurtosis      {res} 52.93461

                 {txt}w_sai_detamt_clmtreal_omit
{hline 61}
      Percentiles      Smallest
 1%    {res}-2.94e+07      -5.81e+07
{txt} 5%    {res}-1.63e+07      -5.59e+07
{txt}10%    {res} -8690952      -3.06e+07       {txt}Obs         {res}        425
{txt}25%    {res} -2390781      -3.01e+07       {txt}Sum of wgt. {res}        425

{txt}50%    {res}  -406054                      {txt}Mean          {res}-274732.4
                        {txt}Largest       Std. dev.     {res} 1.40e+07
{txt}75%    {res} 642120.5       5.45e+07
{txt}90%    {res}  3864434       8.17e+07       {txt}Variance      {res} 1.97e+14
{txt}95%    {res}  9499331       9.90e+07       {txt}Skewness      {res} 4.947733
{txt}99%    {res} 5.37e+07       1.60e+08       {txt}Kurtosis      {res} 52.94676
{txt}
{com}. correlate sai_detamt_clmtreal  w_sai_detamt_clmtreal  if gubapptauth_partisan_rescaled3==1
{txt}(obs=425)

             {c |} sai_de~l w_sai_~l
{hline 13}{c +}{hline 18}
sai_detamt~l {c |}{res}   1.0000
{txt}w_sai_deta~l {c |}{res}   0.7131   1.0000

{txt}
{com}. *
. *
. *
. * Set Global Macro of IQR
. sum w_sai_detamt_clmtreal if gubapptauth_partisan_rescaled3==1, detail

                    {txt}w_sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res}-2.94e+07      -5.81e+07
{txt} 5%    {res}-1.63e+07      -5.59e+07
{txt}10%    {res} -8690952      -3.06e+07       {txt}Obs         {res}        425
{txt}25%    {res} -2390781      -3.01e+07       {txt}Sum of wgt. {res}        425

{txt}50%    {res}-418057.4                      {txt}Mean          {res}-276521.8
                        {txt}Largest       Std. dev.     {res} 1.40e+07
{txt}75%    {res} 625595.5       5.45e+07
{txt}90%    {res}  3864434       8.17e+07       {txt}Variance      {res} 1.97e+14
{txt}95%    {res}  9499331       9.90e+07       {txt}Skewness      {res} 4.947759
{txt}99%    {res} 5.37e+07       1.60e+08       {txt}Kurtosis      {res} 52.93461
{txt}
{com}. global amtiqr_m1m3_rescaled3_1 = round(r(p75),1)-round(r(p25),1)
{txt}
{com}. di $amtiqr_m1m3_rescaled3_1
{res}3016376
{txt}
{com}. 
. sum w_sai_detamt_clmtreal_omit if gubapptauth_partisan_rescaled3==1, detail

                 {txt}w_sai_detamt_clmtreal_omit
{hline 61}
      Percentiles      Smallest
 1%    {res}-2.94e+07      -5.81e+07
{txt} 5%    {res}-1.63e+07      -5.59e+07
{txt}10%    {res} -8690952      -3.06e+07       {txt}Obs         {res}        425
{txt}25%    {res} -2390781      -3.01e+07       {txt}Sum of wgt. {res}        425

{txt}50%    {res}  -406054                      {txt}Mean          {res}-274732.4
                        {txt}Largest       Std. dev.     {res} 1.40e+07
{txt}75%    {res} 642120.5       5.45e+07
{txt}90%    {res}  3864434       8.17e+07       {txt}Variance      {res} 1.97e+14
{txt}95%    {res}  9499331       9.90e+07       {txt}Skewness      {res} 4.947733
{txt}99%    {res} 5.37e+07       1.60e+08       {txt}Kurtosis      {res} 52.94676
{txt}
{com}. global amtiqr_m2m4_rescaled3_1 = round(r(p75),1)-round(r(p25),1)
{txt}
{com}. di $amtiqr_m2m4_rescaled3_1
{res}3032901
{txt}
{com}. 
. 
. 
. * compute interquartile range difference (change from 25th percentile values to 75th percentile values) for above within-state measures * 
. 
. * w_sai_detamt_clmtreal_0.25 -->  w_sai_detamt_clmtreal_0.75 = $3,016,376.5
. display 625595.5  - -2390781 
{res}3016376.5
{txt}
{com}. 
. *
. 
. * compute interquartile range difference (change from 25th percentile values to 75th percentile values) for above within-state measures [RESTRICTED SAMPLE] *  
. 
. * w_sai_detamt_clmtreal_omit_0.25 -->  w_sai_detamt_clmtreal_omit_0.75 = $3,032,901.5
. display  642120.5  - -2390781  
{res}3032901.5
{txt}
{com}. 
. 
. 
. 
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. 
. 
. 
. * Direct gubernatorial appointment regime [gubapptauth_partisan_rescaled3==2] * 
. sum sai_detamt_clmtreal  w_sai_detamt_clmtreal w_sai_detamt_clmtreal_omit if gubapptauth_partisan_rescaled3==2, detail

                     {txt}sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res} 248776.9       137599.1
{txt} 5%    {res} 433172.5       139155.7
{txt}10%    {res} 698678.8       147937.9       {txt}Obs         {res}        402
{txt}25%    {res}  1699557       172050.5       {txt}Sum of wgt. {res}        402

{txt}50%    {res}  4768907                      {txt}Mean          {res} 1.51e+07
                        {txt}Largest       Std. dev.     {res} 3.57e+07
{txt}75%    {res} 1.26e+07       2.05e+08
{txt}90%    {res} 3.96e+07       2.39e+08       {txt}Variance      {res} 1.27e+15
{txt}95%    {res} 5.64e+07       2.45e+08       {txt}Skewness      {res} 7.444859
{txt}99%    {res} 1.50e+08       4.74e+08       {txt}Kurtosis      {res}  79.7754

                    {txt}w_sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res}-4.79e+07      -5.71e+07
{txt} 5%    {res}-2.61e+07      -5.45e+07
{txt}10%    {res} -8587040      -4.97e+07       {txt}Obs         {res}        402
{txt}25%    {res} -3087229      -4.88e+07       {txt}Sum of wgt. {res}        402

{txt}50%    {res}-736467.6                      {txt}Mean          {res} 526940.5
                        {txt}Largest       Std. dev.     {res} 2.84e+07
{txt}75%    {res} 581804.5       1.54e+08
{txt}90%    {res}  3664226       1.61e+08       {txt}Variance      {res} 8.09e+14
{txt}95%    {res} 1.42e+07       1.85e+08       {txt}Skewness      {res} 8.395705
{txt}99%    {res} 1.10e+08       3.96e+08       {txt}Kurtosis      {res} 103.9503

                 {txt}w_sai_detamt_clmtreal_omit
{hline 61}
      Percentiles      Smallest
 1%    {res}-4.79e+07      -5.71e+07
{txt} 5%    {res}-2.61e+07      -5.45e+07
{txt}10%    {res} -8587040      -4.97e+07       {txt}Obs         {res}        402
{txt}25%    {res} -3087229      -4.88e+07       {txt}Sum of wgt. {res}        402

{txt}50%    {res}-690262.3                      {txt}Mean          {res} 523815.6
                        {txt}Largest       Std. dev.     {res} 2.84e+07
{txt}75%    {res} 583312.6       1.54e+08
{txt}90%    {res}  3664226       1.61e+08       {txt}Variance      {res} 8.09e+14
{txt}95%    {res} 1.42e+07       1.85e+08       {txt}Skewness      {res} 8.395611
{txt}99%    {res} 1.10e+08       3.96e+08       {txt}Kurtosis      {res} 103.9479
{txt}
{com}. correlate sai_detamt_clmtreal  w_sai_detamt_clmtreal if gubapptauth_partisan_rescaled3==2
{txt}(obs=402)

             {c |} sai_de~l w_sai_~l
{hline 13}{c +}{hline 18}
sai_detamt~l {c |}{res}   1.0000
{txt}w_sai_deta~l {c |}{res}   0.7845   1.0000

{txt}
{com}. 
. *
. *
. *
. 
. * compute interquartile range difference (change from 25th percentile values to 75th percentile values) for above within-state measures *  
. 
. * Set Global Macro of IQR
. sum w_sai_detamt_clmtreal if gubapptauth_partisan_rescaled3==2, detail

                    {txt}w_sai_detamt_clmtreal
{hline 61}
      Percentiles      Smallest
 1%    {res}-4.79e+07      -5.71e+07
{txt} 5%    {res}-2.61e+07      -5.45e+07
{txt}10%    {res} -8587040      -4.97e+07       {txt}Obs         {res}        402
{txt}25%    {res} -3087229      -4.88e+07       {txt}Sum of wgt. {res}        402

{txt}50%    {res}-736467.6                      {txt}Mean          {res} 526940.5
                        {txt}Largest       Std. dev.     {res} 2.84e+07
{txt}75%    {res} 581804.5       1.54e+08
{txt}90%    {res}  3664226       1.61e+08       {txt}Variance      {res} 8.09e+14
{txt}95%    {res} 1.42e+07       1.85e+08       {txt}Skewness      {res} 8.395705
{txt}99%    {res} 1.10e+08       3.96e+08       {txt}Kurtosis      {res} 103.9503
{txt}
{com}. global amtiqr_m1m3_rescaled3_2 = round(r(p75),1)-round(r(p25),1)
{txt}
{com}. di $amtiqr_m1m3_rescaled3_2
{res}3669034
{txt}
{com}. 
. * w_sai_detamt_clmtreal_0.25 -->  w_sai_detamt_clmtreal_0.75 = $3,669,033.5 
. display 581804.5 - -3087229
{res}3669033.5
{txt}
{com}. 
. *
. *
. *
. 
. * compute interquartile range difference (change from 25th percentile values to 75th percentile values) for above within-state measures [RESTRICTED SAMPLE] * 
. 
. sum w_sai_detamt_clmtreal_omit if gubapptauth_partisan_rescaled3==2, detail

                 {txt}w_sai_detamt_clmtreal_omit
{hline 61}
      Percentiles      Smallest
 1%    {res}-4.79e+07      -5.71e+07
{txt} 5%    {res}-2.61e+07      -5.45e+07
{txt}10%    {res} -8587040      -4.97e+07       {txt}Obs         {res}        402
{txt}25%    {res} -3087229      -4.88e+07       {txt}Sum of wgt. {res}        402

{txt}50%    {res}-690262.3                      {txt}Mean          {res} 523815.6
                        {txt}Largest       Std. dev.     {res} 2.84e+07
{txt}75%    {res} 583312.6       1.54e+08
{txt}90%    {res}  3664226       1.61e+08       {txt}Variance      {res} 8.09e+14
{txt}95%    {res} 1.42e+07       1.85e+08       {txt}Skewness      {res} 8.395611
{txt}99%    {res} 1.10e+08       3.96e+08       {txt}Kurtosis      {res} 103.9479
{txt}
{com}. global amtiqr_m2m4_rescaled3_2 =  round(r(p75),1)-round(r(p25),1)
{txt}
{com}. di $amtiqr_m2m4_rescaled3_2
{res}3670542
{txt}
{com}. 
. * w_sai_detamt_clmtreal_omit_0.25 -->  w_sai_detamt_clmtreal_omit_0.75 = $3,670,541.6 
. display 583312.6 - -3087229
{res}3670541.6
{txt}
{com}. 
. 
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. *** MODELS 3 & 4: PARTISAN BASELINE MODELS -- DISTINCTION BETWEEN DEMOCRATIC AND REPUBLICAN GUBERNATORIAL DIRECT APPOINTMENT POWERS VERSUS ABSENCE OF SUCH INSTITUTIONAL POWERS [THREE GROUP CATEGORICAL MEASURE] ***
. 
. 
. *** PARTISAN BASELINE MODELS: DISTINCTION BETWEEN DEMOCRATIC AND REPUBLICAN GUBERNATORIAL DIRECT APPOINTMENT POWERS VERSUS ABSENCE OF SUCH INSTITUTIONAL POWERS  [THREE GROUP CATEGORICAL MEASURE] ***
. *** gubapptauth_partisan_rescaled3==0 [ABSENCE OF DIRECT GUBERNATORIAL APPOINTMENT POWERS] &  gubapptauth_partisan_rescaled3==1 [REPUBLICAN DIRECT GUBERNATORIAL APPOINTMENT POWERS: CONSTRAINED & UNCONSTRAINED COMBINED] & gubapptauth_partisan_rescaled3==2 [DEMOCRATIC DIRECT GUBERNATORIAL APPOINTMENT POWERS: CONSTRAINED & UNCONSTRAINED COMBINED] ***
. 
. 
. 
. 
. 
. 
. *** ESTIMATION OF MODELS 3 & 4: BENEFIT OVERPAYMENT ERROR DETECTION BY STATE UIP AGENCY-INITIATION [CLAIMANT-/NON-FRAUD] -- AMOUNTS IN 2010 CONSTANT-DOLLAR TERMS  *** 
. 
. 
. ** MODEL 3 **
. 
. glm  sai_detamt_clmtreal i.gubapptauth_partisan_rescaled3  electionyear econideol_median  publicunion_cov  unemp_rate  ln_uiadmin_budget_real ln_pop_size  ln_clmterror_est  ln_employerappeals_ct   i.state_numident i.year,  family(normal) link(log) vce(cluster state_numident)
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -18107.15}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-17783.626}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-17541.174}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-17532.923}  
Iteration 4:{space 2}Log pseudolikelihood = {res: -17532.66}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-17532.652}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-17532.652}  
{res}
{txt}Generalized linear models{col 51}Number of obs{col 67}={col 69}{res}       999
{txt}Optimization     : {res}ML{txt}{col 51}Residual df{col 67}={col 69}{res}       967
{col 20}{txt}{col 51}Scale parameter{col 67}={col 70}{res} 1.11e+14
{txt}Deviance{col 18}={res}{col 20} 1.02566e+17{txt}{col 51}(1/df) Deviance{col 67}={res}{col 70} 1.06e+14
{txt}Pearson{col 18}={res}{col 20} 1.02566e+17{txt}{col 51}(1/df) Pearson{col 67}={res}{col 70} 1.06e+14

{txt}Variance function: {res}V(u) = {col 27}1{col 51}{txt}[{res}Gaussian{txt}]
Link function    : {res}g(u) = {col 27}ln(u){col 51}{txt}[{res}Log{txt}]

{col 51}{help j_glmic##|_new:AIC}{col 67}={res}{col 70} 35.16447
{txt}Log pseudolikelihood{col 22}= {res}-17532.65213{txt}{col 51}{help j_glmic##|_new:BIC}{col 67}={res}{col 70} 1.03e+17

{txt}{ralign 84:(Std. err. adjusted for {res:50} clusters in {res:state_numident})}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}sai_detamt_clmtr~l{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gubapptauth_part~3 {c |}
{space 16}1  {c |}{col 20}{res}{space 2} .2637775{col 32}{space 2} .1380084{col 43}{space 1}    1.91{col 52}{space 3}0.056{col 60}{space 4}-.0067139{col 73}{space 3} .5342689
{txt}{space 16}2  {c |}{col 20}{res}{space 2}-.5045077{col 32}{space 2}  .376702{col 43}{space 1}   -1.34{col 52}{space 3}0.180{col 60}{space 4} -1.24283{col 73}{space 3} .2338146
{txt}{space 18} {c |}
{space 6}electionyear {c |}{col 20}{res}{space 2} .1582151{col 32}{space 2} .0940488{col 43}{space 1}    1.68{col 52}{space 3}0.093{col 60}{space 4}-.0261172{col 73}{space 3} .3425474
{txt}{space 2}econideol_median {c |}{col 20}{res}{space 2} 1.115563{col 32}{space 2} .2436628{col 43}{space 1}    4.58{col 52}{space 3}0.000{col 60}{space 4} .6379924{col 73}{space 3} 1.593133
{txt}{space 3}publicunion_cov {c |}{col 20}{res}{space 2} -.018698{col 32}{space 2}  .022108{col 43}{space 1}   -0.85{col 52}{space 3}0.398{col 60}{space 4}-.0620289{col 73}{space 3} .0246329
{txt}{space 8}unemp_rate {c |}{col 20}{res}{space 2}-.0853997{col 32}{space 2} .0292203{col 43}{space 1}   -2.92{col 52}{space 3}0.003{col 60}{space 4}-.1426704{col 73}{space 3} -.028129
{txt}ln_uiadmin_budge~l {c |}{col 20}{res}{space 2} 1.689578{col 32}{space 2} .5799508{col 43}{space 1}    2.91{col 52}{space 3}0.004{col 60}{space 4} .5528951{col 73}{space 3}  2.82626
{txt}{space 7}ln_pop_size {c |}{col 20}{res}{space 2} .0644152{col 32}{space 2} .4087181{col 43}{space 1}    0.16{col 52}{space 3}0.875{col 60}{space 4}-.7366575{col 73}{space 3} .8654879
{txt}{space 2}ln_clmterror_est {c |}{col 20}{res}{space 2} .1118099{col 32}{space 2}  .120154{col 43}{space 1}    0.93{col 52}{space 3}0.352{col 60}{space 4}-.1236876{col 73}{space 3} .3473073
{txt}ln_employerappea~t {c |}{col 20}{res}{space 2} -.368129{col 32}{space 2} .2068258{col 43}{space 1}   -1.78{col 52}{space 3}0.075{col 60}{space 4}   -.7735{col 73}{space 3} .0372421
{txt}{space 18} {c |}
{space 4}state_numident {c |}
{space 16}2  {c |}{col 20}{res}{space 2}-2.266928{col 32}{space 2}  .704175{col 43}{space 1}   -3.22{col 52}{space 3}0.001{col 60}{space 4}-3.647086{col 73}{space 3}-.8867704
{txt}{space 16}3  {c |}{col 20}{res}{space 2}-.9653804{col 32}{space 2} .2853101{col 43}{space 1}   -3.38{col 52}{space 3}0.001{col 60}{space 4}-1.524578{col 73}{space 3}-.4061829
{txt}{space 16}4  {c |}{col 20}{res}{space 2}-.9542838{col 32}{space 2} .5150707{col 43}{space 1}   -1.85{col 52}{space 3}0.064{col 60}{space 4}-1.963804{col 73}{space 3} .0552362
{txt}{space 16}5  {c |}{col 20}{res}{space 2}-8.216461{col 32}{space 2} 2.251072{col 43}{space 1}   -3.65{col 52}{space 3}0.000{col 60}{space 4}-12.62848{col 73}{space 3}-3.804441
{txt}{space 16}6  {c |}{col 20}{res}{space 2}-1.020855{col 32}{space 2} .3979495{col 43}{space 1}   -2.57{col 52}{space 3}0.010{col 60}{space 4}-1.800821{col 73}{space 3}-.2408881
{txt}{space 16}7  {c |}{col 20}{res}{space 2}-5.884433{col 32}{space 2} 1.346031{col 43}{space 1}   -4.37{col 52}{space 3}0.000{col 60}{space 4}-8.522604{col 73}{space 3}-3.246261
{txt}{space 16}8  {c |}{col 20}{res}{space 2}-2.324518{col 32}{space 2} .6123716{col 43}{space 1}   -3.80{col 52}{space 3}0.000{col 60}{space 4}-3.524744{col 73}{space 3}-1.124292
{txt}{space 16}9  {c |}{col 20}{res}{space 2}-.0963368{col 32}{space 2} .4095382{col 43}{space 1}   -0.24{col 52}{space 3}0.814{col 60}{space 4} -.899017{col 73}{space 3} .7063433
{txt}{space 15}10  {c |}{col 20}{res}{space 2}-1.634674{col 32}{space 2} .3849777{col 43}{space 1}   -4.25{col 52}{space 3}0.000{col 60}{space 4}-2.389216{col 73}{space 3}-.8801313
{txt}{space 15}11  {c |}{col 20}{res}{space 2}-3.525368{col 32}{space 2} .6383254{col 43}{space 1}   -5.52{col 52}{space 3}0.000{col 60}{space 4}-4.776463{col 73}{space 3}-2.274273
{txt}{space 15}12  {c |}{col 20}{res}{space 2}-.9890155{col 32}{space 2} .4383155{col 43}{space 1}   -2.26{col 52}{space 3}0.024{col 60}{space 4}-1.848098{col 73}{space 3} -.129933
{txt}{space 15}13  {c |}{col 20}{res}{space 2}-2.396528{col 32}{space 2} 1.139101{col 43}{space 1}   -2.10{col 52}{space 3}0.035{col 60}{space 4}-4.629125{col 73}{space 3}-.1639324
{txt}{space 15}14  {c |}{col 20}{res}{space 2} -.680935{col 32}{space 2} .2667831{col 43}{space 1}   -2.55{col 52}{space 3}0.011{col 60}{space 4} -1.20382{col 73}{space 3}-.1580498
{txt}{space 15}15  {c |}{col 20}{res}{space 2} -1.88329{col 32}{space 2} .3685301{col 43}{space 1}   -5.11{col 52}{space 3}0.000{col 60}{space 4}-2.605596{col 73}{space 3}-1.160985
{txt}{space 15}16  {c |}{col 20}{res}{space 2}-.3883094{col 32}{space 2} .2731358{col 43}{space 1}   -1.42{col 52}{space 3}0.155{col 60}{space 4}-.9236458{col 73}{space 3}  .147027
{txt}{space 15}17  {c |}{col 20}{res}{space 2}-1.182573{col 32}{space 2} .3341492{col 43}{space 1}   -3.54{col 52}{space 3}0.000{col 60}{space 4}-1.837494{col 73}{space 3}-.5276531
{txt}{space 15}18  {c |}{col 20}{res}{space 2}-.3581438{col 32}{space 2} .3870298{col 43}{space 1}   -0.93{col 52}{space 3}0.355{col 60}{space 4}-1.116708{col 73}{space 3} .4004206
{txt}{space 15}19  {c |}{col 20}{res}{space 2} -3.24173{col 32}{space 2}  .809545{col 43}{space 1}   -4.00{col 52}{space 3}0.000{col 60}{space 4}-4.828409{col 73}{space 3}-1.655051
{txt}{space 15}20  {c |}{col 20}{res}{space 2}-6.508216{col 32}{space 2} 1.536014{col 43}{space 1}   -4.24{col 52}{space 3}0.000{col 60}{space 4}-9.518749{col 73}{space 3}-3.497683
{txt}{space 15}21  {c |}{col 20}{res}{space 2}-5.890608{col 32}{space 2} 1.405624{col 43}{space 1}   -4.19{col 52}{space 3}0.000{col 60}{space 4} -8.64558{col 73}{space 3}-3.135636
{txt}{space 15}22  {c |}{col 20}{res}{space 2}-1.442506{col 32}{space 2} 1.252793{col 43}{space 1}   -1.15{col 52}{space 3}0.250{col 60}{space 4}-3.897935{col 73}{space 3} 1.012923
{txt}{space 15}23  {c |}{col 20}{res}{space 2}-1.749304{col 32}{space 2} .9020351{col 43}{space 1}   -1.94{col 52}{space 3}0.052{col 60}{space 4} -3.51726{col 73}{space 3} .0186524
{txt}{space 15}24  {c |}{col 20}{res}{space 2} .4394637{col 32}{space 2} .5667639{col 43}{space 1}    0.78{col 52}{space 3}0.438{col 60}{space 4}-.6713732{col 73}{space 3} 1.550301
{txt}{space 15}25  {c |}{col 20}{res}{space 2} -.954119{col 32}{space 2} .2315379{col 43}{space 1}   -4.12{col 52}{space 3}0.000{col 60}{space 4}-1.407925{col 73}{space 3} -.500313
{txt}{space 15}26  {c |}{col 20}{res}{space 2}-2.486991{col 32}{space 2} .6404513{col 43}{space 1}   -3.88{col 52}{space 3}0.000{col 60}{space 4}-3.742253{col 73}{space 3} -1.23173
{txt}{space 15}27  {c |}{col 20}{res}{space 2}-2.317545{col 32}{space 2} .4565261{col 43}{space 1}   -5.08{col 52}{space 3}0.000{col 60}{space 4}-3.212319{col 73}{space 3} -1.42277
{txt}{space 15}28  {c |}{col 20}{res}{space 2} .9219751{col 32}{space 2} .4420517{col 43}{space 1}    2.09{col 52}{space 3}0.037{col 60}{space 4} .0555697{col 73}{space 3}  1.78838
{txt}{space 15}29  {c |}{col 20}{res}{space 2} -3.37391{col 32}{space 2} .6405499{col 43}{space 1}   -5.27{col 52}{space 3}0.000{col 60}{space 4}-4.629365{col 73}{space 3}-2.118456
{txt}{space 15}30  {c |}{col 20}{res}{space 2}-2.547962{col 32}{space 2} 1.398256{col 43}{space 1}   -1.82{col 52}{space 3}0.068{col 60}{space 4}-5.288494{col 73}{space 3} .1925704
{txt}{space 15}31  {c |}{col 20}{res}{space 2}-1.812521{col 32}{space 2} .5258674{col 43}{space 1}   -3.45{col 52}{space 3}0.001{col 60}{space 4}-2.843202{col 73}{space 3}-.7818394
{txt}{space 15}32  {c |}{col 20}{res}{space 2}-7.303589{col 32}{space 2} 2.041638{col 43}{space 1}   -3.58{col 52}{space 3}0.000{col 60}{space 4}-11.30513{col 73}{space 3}-3.302052
{txt}{space 15}33  {c |}{col 20}{res}{space 2}-.6563453{col 32}{space 2} .4260593{col 43}{space 1}   -1.54{col 52}{space 3}0.123{col 60}{space 4}-1.491406{col 73}{space 3} .1787156
{txt}{space 15}34  {c |}{col 20}{res}{space 2}-.5524089{col 32}{space 2}  .761059{col 43}{space 1}   -0.73{col 52}{space 3}0.468{col 60}{space 4}-2.044057{col 73}{space 3} .9392394
{txt}{space 15}35  {c |}{col 20}{res}{space 2}-2.575455{col 32}{space 2} 1.027644{col 43}{space 1}   -2.51{col 52}{space 3}0.012{col 60}{space 4}  -4.5896{col 73}{space 3}-.5613105
{txt}{space 15}36  {c |}{col 20}{res}{space 2}-1.977028{col 32}{space 2} .3604043{col 43}{space 1}   -5.49{col 52}{space 3}0.000{col 60}{space 4}-2.683407{col 73}{space 3}-1.270649
{txt}{space 15}37  {c |}{col 20}{res}{space 2}-5.955956{col 32}{space 2} 1.461427{col 43}{space 1}   -4.08{col 52}{space 3}0.000{col 60}{space 4}-8.820299{col 73}{space 3}-3.091612
{txt}{space 15}38  {c |}{col 20}{res}{space 2}-2.615321{col 32}{space 2} 1.200721{col 43}{space 1}   -2.18{col 52}{space 3}0.029{col 60}{space 4}-4.968692{col 73}{space 3}-.2619507
{txt}{space 15}39  {c |}{col 20}{res}{space 2}-2.945607{col 32}{space 2}  .951302{col 43}{space 1}   -3.10{col 52}{space 3}0.002{col 60}{space 4}-4.810125{col 73}{space 3} -1.08109
{txt}{space 15}40  {c |}{col 20}{res}{space 2}-.3796617{col 32}{space 2} .5121566{col 43}{space 1}   -0.74{col 52}{space 3}0.459{col 60}{space 4} -1.38347{col 73}{space 3} .6241468
{txt}{space 15}41  {c |}{col 20}{res}{space 2}-1.411316{col 32}{space 2} .7253408{col 43}{space 1}   -1.95{col 52}{space 3}0.052{col 60}{space 4}-2.832958{col 73}{space 3} .0103256
{txt}{space 15}42  {c |}{col 20}{res}{space 2}-.9595561{col 32}{space 2} .2633781{col 43}{space 1}   -3.64{col 52}{space 3}0.000{col 60}{space 4}-1.475768{col 73}{space 3}-.4433444
{txt}{space 15}43  {c |}{col 20}{res}{space 2}-.2091007{col 32}{space 2} .6129197{col 43}{space 1}   -0.34{col 52}{space 3}0.733{col 60}{space 4}-1.410401{col 73}{space 3} .9921998
{txt}{space 15}44  {c |}{col 20}{res}{space 2}-.8370233{col 32}{space 2} .3411272{col 43}{space 1}   -2.45{col 52}{space 3}0.014{col 60}{space 4} -1.50562{col 73}{space 3}-.1684264
{txt}{space 15}45  {c |}{col 20}{res}{space 2}-5.177162{col 32}{space 2} 1.069619{col 43}{space 1}   -4.84{col 52}{space 3}0.000{col 60}{space 4}-7.273576{col 73}{space 3}-3.080748
{txt}{space 15}46  {c |}{col 20}{res}{space 2}-2.111823{col 32}{space 2} .5353558{col 43}{space 1}   -3.94{col 52}{space 3}0.000{col 60}{space 4}-3.161101{col 73}{space 3}-1.062545
{txt}{space 15}47  {c |}{col 20}{res}{space 2}-.5484437{col 32}{space 2} 1.045143{col 43}{space 1}   -0.52{col 52}{space 3}0.600{col 60}{space 4}-2.596886{col 73}{space 3} 1.499998
{txt}{space 15}48  {c |}{col 20}{res}{space 2}-.5232907{col 32}{space 2} .4869974{col 43}{space 1}   -1.07{col 52}{space 3}0.283{col 60}{space 4}-1.477788{col 73}{space 3} .4312066
{txt}{space 15}49  {c |}{col 20}{res}{space 2}-2.143084{col 32}{space 2} .5980309{col 43}{space 1}   -3.58{col 52}{space 3}0.000{col 60}{space 4}-3.315203{col 73}{space 3}-.9709653
{txt}{space 15}50  {c |}{col 20}{res}{space 2}-.2611819{col 32}{space 2} .7460423{col 43}{space 1}   -0.35{col 52}{space 3}0.726{col 60}{space 4}-1.723398{col 73}{space 3} 1.201034
{txt}{space 18} {c |}
{space 14}year {c |}
{space 13}2003  {c |}{col 20}{res}{space 2} .8092886{col 32}{space 2} .2875624{col 43}{space 1}    2.81{col 52}{space 3}0.005{col 60}{space 4} .2456767{col 73}{space 3} 1.372901
{txt}{space 13}2004  {c |}{col 20}{res}{space 2} .9413799{col 32}{space 2} .4495347{col 43}{space 1}    2.09{col 52}{space 3}0.036{col 60}{space 4} .0603081{col 73}{space 3} 1.822452
{txt}{space 13}2005  {c |}{col 20}{res}{space 2} .6667426{col 32}{space 2} .5872607{col 43}{space 1}    1.14{col 52}{space 3}0.256{col 60}{space 4}-.4842672{col 73}{space 3} 1.817752
{txt}{space 13}2006  {c |}{col 20}{res}{space 2} .5301602{col 32}{space 2} .5724408{col 43}{space 1}    0.93{col 52}{space 3}0.354{col 60}{space 4}-.5918031{col 73}{space 3} 1.652124
{txt}{space 13}2007  {c |}{col 20}{res}{space 2} .7456237{col 32}{space 2} .5562364{col 43}{space 1}    1.34{col 52}{space 3}0.180{col 60}{space 4}-.3445797{col 73}{space 3} 1.835827
{txt}{space 13}2008  {c |}{col 20}{res}{space 2} .9745112{col 32}{space 2} .4627622{col 43}{space 1}    2.11{col 52}{space 3}0.035{col 60}{space 4} .0675139{col 73}{space 3} 1.881509
{txt}{space 13}2009  {c |}{col 20}{res}{space 2} 1.708448{col 32}{space 2} .3081055{col 43}{space 1}    5.55{col 52}{space 3}0.000{col 60}{space 4} 1.104573{col 73}{space 3} 2.312324
{txt}{space 13}2010  {c |}{col 20}{res}{space 2} 1.832092{col 32}{space 2}  .467198{col 43}{space 1}    3.92{col 52}{space 3}0.000{col 60}{space 4} .9164006{col 73}{space 3} 2.747783
{txt}{space 13}2011  {c |}{col 20}{res}{space 2} 1.729773{col 32}{space 2} .4887674{col 43}{space 1}    3.54{col 52}{space 3}0.000{col 60}{space 4} .7718062{col 73}{space 3} 2.687739
{txt}{space 13}2012  {c |}{col 20}{res}{space 2} 1.460988{col 32}{space 2} .4435672{col 43}{space 1}    3.29{col 52}{space 3}0.001{col 60}{space 4} .5916119{col 73}{space 3} 2.330363
{txt}{space 13}2013  {c |}{col 20}{res}{space 2} 1.581899{col 32}{space 2} .4095062{col 43}{space 1}    3.86{col 52}{space 3}0.000{col 60}{space 4} .7792817{col 73}{space 3} 2.384517
{txt}{space 13}2014  {c |}{col 20}{res}{space 2}  1.12566{col 32}{space 2}    .4128{col 43}{space 1}    2.73{col 52}{space 3}0.006{col 60}{space 4} .3165872{col 73}{space 3} 1.934733
{txt}{space 13}2015  {c |}{col 20}{res}{space 2}  1.03232{col 32}{space 2} .6356906{col 43}{space 1}    1.62{col 52}{space 3}0.104{col 60}{space 4}-.2136106{col 73}{space 3} 2.278251
{txt}{space 13}2016  {c |}{col 20}{res}{space 2} .5821894{col 32}{space 2} .7204366{col 43}{space 1}    0.81{col 52}{space 3}0.419{col 60}{space 4}-.8298404{col 73}{space 3} 1.994219
{txt}{space 13}2017  {c |}{col 20}{res}{space 2} .5821013{col 32}{space 2} .7944982{col 43}{space 1}    0.73{col 52}{space 3}0.464{col 60}{space 4}-.9750866{col 73}{space 3} 2.139289
{txt}{space 13}2018  {c |}{col 20}{res}{space 2} .9234086{col 32}{space 2}  .587473{col 43}{space 1}    1.57{col 52}{space 3}0.116{col 60}{space 4}-.2280173{col 73}{space 3} 2.074834
{txt}{space 13}2019  {c |}{col 20}{res}{space 2} .6637532{col 32}{space 2} .6591073{col 43}{space 1}    1.01{col 52}{space 3}0.314{col 60}{space 4}-.6280733{col 73}{space 3}  1.95558
{txt}{space 13}2020  {c |}{col 20}{res}{space 2} 2.321947{col 32}{space 2} .4690856{col 43}{space 1}    4.95{col 52}{space 3}0.000{col 60}{space 4} 1.402557{col 73}{space 3} 3.241338
{txt}{space 13}2021  {c |}{col 20}{res}{space 2} 1.986693{col 32}{space 2} .6027627{col 43}{space 1}    3.30{col 52}{space 3}0.001{col 60}{space 4} .8052997{col 73}{space 3} 3.168086
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}-12.34225{col 32}{space 2} 7.964052{col 43}{space 1}   -1.55{col 52}{space 3}0.121{col 60}{space 4}-27.95151{col 73}{space 3} 3.267004
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       999{col 28}        .{col 39}-17532.65{col 50}    32{col 58}  35129.3{col 69} 35286.32
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. *
. *
. estimate store m3
{txt}
{com}. *
. *
. *
. 
. 
. ** MODEL 4 **
. 
. glm  sai_detamt_clmtreal  i.gubapptauth_partisan_rescaled3  electionyear    econideol_median  publicunion_cov  unemp_rate  ln_uiadmin_budget_real ln_pop_size  ln_clmterror_est ln_employerappeals_ct  i.state_numident i.year if nonpartisan_gubapprove!=1,  family(normal) link(log) vce(cluster state_numident)
{res}
{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-17271.517}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -16965.19}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-16737.902}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-16730.033}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-16729.773}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-16729.766}  
Iteration 6:{space 2}Log pseudolikelihood = {res:-16729.765}  
{res}
{txt}Generalized linear models{col 51}Number of obs{col 67}={col 69}{res}       952
{txt}Optimization     : {res}ML{txt}{col 51}Residual df{col 67}={col 69}{res}       921
{col 20}{txt}{col 51}Scale parameter{col 67}={col 70}{res} 1.17e+14
{txt}Deviance{col 18}={res}{col 20} 1.02358e+17{txt}{col 51}(1/df) Deviance{col 67}={res}{col 70} 1.11e+14
{txt}Pearson{col 18}={res}{col 20} 1.02358e+17{txt}{col 51}(1/df) Pearson{col 67}={res}{col 70} 1.11e+14

{txt}Variance function: {res}V(u) = {col 27}1{col 51}{txt}[{res}Gaussian{txt}]
Link function    : {res}g(u) = {col 27}ln(u){col 51}{txt}[{res}Log{txt}]

{col 51}{help j_glmic##|_new:AIC}{col 67}={res}{col 70} 35.21169
{txt}Log pseudolikelihood{col 22}= {res}-16729.76549{txt}{col 51}{help j_glmic##|_new:BIC}{col 67}={res}{col 70} 1.02e+17

{txt}{ralign 84:(Std. err. adjusted for {res:48} clusters in {res:state_numident})}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}sai_detamt_clmtr~l{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gubapptauth_part~3 {c |}
{space 16}1  {c |}{col 20}{res}{space 2} .2741483{col 32}{space 2} .1385236{col 43}{space 1}    1.98{col 52}{space 3}0.048{col 60}{space 4} .0026471{col 73}{space 3} .5456495
{txt}{space 16}2  {c |}{col 20}{res}{space 2}-.4935221{col 32}{space 2} .3752789{col 43}{space 1}   -1.32{col 52}{space 3}0.188{col 60}{space 4}-1.229055{col 73}{space 3} .2420112
{txt}{space 18} {c |}
{space 6}electionyear {c |}{col 20}{res}{space 2}  .158625{col 32}{space 2} .0943052{col 43}{space 1}    1.68{col 52}{space 3}0.093{col 60}{space 4}-.0262099{col 73}{space 3} .3434599
{txt}{space 2}econideol_median {c |}{col 20}{res}{space 2} 1.117215{col 32}{space 2} .2437015{col 43}{space 1}    4.58{col 52}{space 3}0.000{col 60}{space 4} .6395687{col 73}{space 3} 1.594861
{txt}{space 3}publicunion_cov {c |}{col 20}{res}{space 2}-.0188586{col 32}{space 2} .0221927{col 43}{space 1}   -0.85{col 52}{space 3}0.395{col 60}{space 4}-.0623554{col 73}{space 3} .0246382
{txt}{space 8}unemp_rate {c |}{col 20}{res}{space 2}-.0860412{col 32}{space 2} .0292422{col 43}{space 1}   -2.94{col 52}{space 3}0.003{col 60}{space 4}-.1433549{col 73}{space 3}-.0287274
{txt}ln_uiadmin_budge~l {c |}{col 20}{res}{space 2} 1.690071{col 32}{space 2}  .581563{col 43}{space 1}    2.91{col 52}{space 3}0.004{col 60}{space 4} .5502285{col 73}{space 3} 2.829914
{txt}{space 7}ln_pop_size {c |}{col 20}{res}{space 2} .0631826{col 32}{space 2} .4099451{col 43}{space 1}    0.15{col 52}{space 3}0.878{col 60}{space 4} -.740295{col 73}{space 3} .8666603
{txt}{space 2}ln_clmterror_est {c |}{col 20}{res}{space 2} .1132344{col 32}{space 2} .1206073{col 43}{space 1}    0.94{col 52}{space 3}0.348{col 60}{space 4}-.1231516{col 73}{space 3} .3496205
{txt}ln_employerappea~t {c |}{col 20}{res}{space 2}-.3673485{col 32}{space 2}  .207197{col 43}{space 1}   -1.77{col 52}{space 3}0.076{col 60}{space 4}-.7734472{col 73}{space 3} .0387502
{txt}{space 18} {c |}
{space 4}state_numident {c |}
{space 16}3  {c |}{col 20}{res}{space 2}-.9645045{col 32}{space 2} .2862833{col 43}{space 1}   -3.37{col 52}{space 3}0.001{col 60}{space 4}-1.525609{col 73}{space 3}-.4033995
{txt}{space 16}4  {c |}{col 20}{res}{space 2}-.9649383{col 32}{space 2} .5155013{col 43}{space 1}   -1.87{col 52}{space 3}0.061{col 60}{space 4}-1.975302{col 73}{space 3} .0454257
{txt}{space 16}5  {c |}{col 20}{res}{space 2}    -8.23{col 32}{space 2} 2.257799{col 43}{space 1}   -3.65{col 52}{space 3}0.000{col 60}{space 4} -12.6552{col 73}{space 3}-3.804795
{txt}{space 16}6  {c |}{col 20}{res}{space 2}  -1.0353{col 32}{space 2} .3963033{col 43}{space 1}   -2.61{col 52}{space 3}0.009{col 60}{space 4} -1.81204{col 73}{space 3}-.2585599
{txt}{space 16}7  {c |}{col 20}{res}{space 2}-5.893082{col 32}{space 2} 1.350819{col 43}{space 1}   -4.36{col 52}{space 3}0.000{col 60}{space 4} -8.54064{col 73}{space 3}-3.245525
{txt}{space 16}8  {c |}{col 20}{res}{space 2}-2.333992{col 32}{space 2} .6125722{col 43}{space 1}   -3.81{col 52}{space 3}0.000{col 60}{space 4}-3.534612{col 73}{space 3}-1.133373
{txt}{space 16}9  {c |}{col 20}{res}{space 2}-.1063792{col 32}{space 2} .4114736{col 43}{space 1}   -0.26{col 52}{space 3}0.796{col 60}{space 4}-.9128527{col 73}{space 3} .7000942
{txt}{space 15}10  {c |}{col 20}{res}{space 2}-1.633963{col 32}{space 2} .3878066{col 43}{space 1}   -4.21{col 52}{space 3}0.000{col 60}{space 4} -2.39405{col 73}{space 3}-.8738759
{txt}{space 15}11  {c |}{col 20}{res}{space 2}-3.530328{col 32}{space 2} .6419944{col 43}{space 1}   -5.50{col 52}{space 3}0.000{col 60}{space 4}-4.788614{col 73}{space 3}-2.272042
{txt}{space 15}12  {c |}{col 20}{res}{space 2}-.9975525{col 32}{space 2} .4392312{col 43}{space 1}   -2.27{col 52}{space 3}0.023{col 60}{space 4} -1.85843{col 73}{space 3}-.1366752
{txt}{space 15}13  {c |}{col 20}{res}{space 2} -2.40657{col 32}{space 2}  1.14331{col 43}{space 1}   -2.10{col 52}{space 3}0.035{col 60}{space 4}-4.647418{col 73}{space 3} -.165723
{txt}{space 15}14  {c |}{col 20}{res}{space 2}-.6918171{col 32}{space 2} .2662609{col 43}{space 1}   -2.60{col 52}{space 3}0.009{col 60}{space 4}-1.213679{col 73}{space 3}-.1699553
{txt}{space 15}15  {c |}{col 20}{res}{space 2}-1.894784{col 32}{space 2} .3692668{col 43}{space 1}   -5.13{col 52}{space 3}0.000{col 60}{space 4}-2.618533{col 73}{space 3}-1.171034
{txt}{space 15}16  {c |}{col 20}{res}{space 2}-.3990442{col 32}{space 2} .2738141{col 43}{space 1}   -1.46{col 52}{space 3}0.145{col 60}{space 4}  -.93571{col 73}{space 3} .1376215
{txt}{space 15}18  {c |}{col 20}{res}{space 2}-.3649492{col 32}{space 2} .3872788{col 43}{space 1}   -0.94{col 52}{space 3}0.346{col 60}{space 4}-1.124002{col 73}{space 3} .3941034
{txt}{space 15}19  {c |}{col 20}{res}{space 2}-3.251265{col 32}{space 2} .8109292{col 43}{space 1}   -4.01{col 52}{space 3}0.000{col 60}{space 4}-4.840657{col 73}{space 3}-1.661873
{txt}{space 15}20  {c |}{col 20}{res}{space 2}-6.520572{col 32}{space 2} 1.539478{col 43}{space 1}   -4.24{col 52}{space 3}0.000{col 60}{space 4}-9.537893{col 73}{space 3} -3.50325
{txt}{space 15}21  {c |}{col 20}{res}{space 2}-5.931846{col 32}{space 2} 1.410816{col 43}{space 1}   -4.20{col 52}{space 3}0.000{col 60}{space 4}-8.696994{col 73}{space 3}-3.166697
{txt}{space 15}22  {c |}{col 20}{res}{space 2}-1.446924{col 32}{space 2} 1.258601{col 43}{space 1}   -1.15{col 52}{space 3}0.250{col 60}{space 4}-3.913736{col 73}{space 3} 1.019889
{txt}{space 15}23  {c |}{col 20}{res}{space 2}-1.762606{col 32}{space 2} .9017347{col 43}{space 1}   -1.95{col 52}{space 3}0.051{col 60}{space 4}-3.529974{col 73}{space 3} .0047615
{txt}{space 15}24  {c |}{col 20}{res}{space 2} .4328034{col 32}{space 2} .5677027{col 43}{space 1}    0.76{col 52}{space 3}0.446{col 60}{space 4}-.6798734{col 73}{space 3}  1.54548
{txt}{space 15}25  {c |}{col 20}{res}{space 2}-.9635144{col 32}{space 2}  .235531{col 43}{space 1}   -4.09{col 52}{space 3}0.000{col 60}{space 4}-1.425147{col 73}{space 3}-.5018821
{txt}{space 15}26  {c |}{col 20}{res}{space 2}-2.493377{col 32}{space 2} .6436121{col 43}{space 1}   -3.87{col 52}{space 3}0.000{col 60}{space 4}-3.754833{col 73}{space 3} -1.23192
{txt}{space 15}27  {c |}{col 20}{res}{space 2}-2.329579{col 32}{space 2} .4543021{col 43}{space 1}   -5.13{col 52}{space 3}0.000{col 60}{space 4}-3.219995{col 73}{space 3}-1.439163
{txt}{space 15}28  {c |}{col 20}{res}{space 2} .9167097{col 32}{space 2} .4469249{col 43}{space 1}    2.05{col 52}{space 3}0.040{col 60}{space 4}  .040753{col 73}{space 3} 1.792666
{txt}{space 15}29  {c |}{col 20}{res}{space 2}-3.379679{col 32}{space 2} .6433239{col 43}{space 1}   -5.25{col 52}{space 3}0.000{col 60}{space 4} -4.64057{col 73}{space 3}-2.118787
{txt}{space 15}30  {c |}{col 20}{res}{space 2} -2.55733{col 32}{space 2} 1.403129{col 43}{space 1}   -1.82{col 52}{space 3}0.068{col 60}{space 4}-5.307412{col 73}{space 3} .1927518
{txt}{space 15}31  {c |}{col 20}{res}{space 2}-1.825403{col 32}{space 2} .5253655{col 43}{space 1}   -3.47{col 52}{space 3}0.001{col 60}{space 4}  -2.8551{col 73}{space 3}-.7957052
{txt}{space 15}32  {c |}{col 20}{res}{space 2}-7.314108{col 32}{space 2} 2.047871{col 43}{space 1}   -3.57{col 52}{space 3}0.000{col 60}{space 4}-11.32786{col 73}{space 3}-3.300354
{txt}{space 15}33  {c |}{col 20}{res}{space 2}-.6694224{col 32}{space 2}  .424182{col 43}{space 1}   -1.58{col 52}{space 3}0.115{col 60}{space 4}-1.500804{col 73}{space 3} .1619592
{txt}{space 15}34  {c |}{col 20}{res}{space 2}-.5612688{col 32}{space 2} .7627317{col 43}{space 1}   -0.74{col 52}{space 3}0.462{col 60}{space 4}-2.056196{col 73}{space 3}  .933658
{txt}{space 15}35  {c |}{col 20}{res}{space 2}-2.584095{col 32}{space 2} 1.031649{col 43}{space 1}   -2.50{col 52}{space 3}0.012{col 60}{space 4} -4.60609{col 73}{space 3}-.5621004
{txt}{space 15}36  {c |}{col 20}{res}{space 2}-1.977077{col 32}{space 2} .3615665{col 43}{space 1}   -5.47{col 52}{space 3}0.000{col 60}{space 4}-2.685734{col 73}{space 3} -1.26842
{txt}{space 15}37  {c |}{col 20}{res}{space 2} -5.96592{col 32}{space 2} 1.465665{col 43}{space 1}   -4.07{col 52}{space 3}0.000{col 60}{space 4} -8.83857{col 73}{space 3} -3.09327
{txt}{space 15}38  {c |}{col 20}{res}{space 2}-2.625346{col 32}{space 2}  1.20486{col 43}{space 1}   -2.18{col 52}{space 3}0.029{col 60}{space 4}-4.986828{col 73}{space 3}-.2638641
{txt}{space 15}39  {c |}{col 20}{res}{space 2}-3.070055{col 32}{space 2} .9555196{col 43}{space 1}   -3.21{col 52}{space 3}0.001{col 60}{space 4}-4.942839{col 73}{space 3}-1.197271
{txt}{space 15}40  {c |}{col 20}{res}{space 2}-.3882524{col 32}{space 2} .5134969{col 43}{space 1}   -0.76{col 52}{space 3}0.450{col 60}{space 4}-1.394688{col 73}{space 3}  .618183
{txt}{space 15}41  {c |}{col 20}{res}{space 2}-1.419407{col 32}{space 2} .7272048{col 43}{space 1}   -1.95{col 52}{space 3}0.051{col 60}{space 4}-2.844702{col 73}{space 3} .0058881
{txt}{space 15}42  {c |}{col 20}{res}{space 2}-.9712096{col 32}{space 2} .2614538{col 43}{space 1}   -3.71{col 52}{space 3}0.000{col 60}{space 4} -1.48365{col 73}{space 3}-.4587695
{txt}{space 15}43  {c |}{col 20}{res}{space 2}-.2117674{col 32}{space 2} .6162802{col 43}{space 1}   -0.34{col 52}{space 3}0.731{col 60}{space 4}-1.419654{col 73}{space 3} .9961195
{txt}{space 15}44  {c |}{col 20}{res}{space 2}-.8471206{col 32}{space 2} .3413946{col 43}{space 1}   -2.48{col 52}{space 3}0.013{col 60}{space 4}-1.516242{col 73}{space 3}-.1779995
{txt}{space 15}45  {c |}{col 20}{res}{space 2}-5.189394{col 32}{space 2} 1.070457{col 43}{space 1}   -4.85{col 52}{space 3}0.000{col 60}{space 4}-7.287452{col 73}{space 3}-3.091337
{txt}{space 15}46  {c |}{col 20}{res}{space 2}-2.127602{col 32}{space 2} .5352295{col 43}{space 1}   -3.98{col 52}{space 3}0.000{col 60}{space 4}-3.176632{col 73}{space 3}-1.078571
{txt}{space 15}47  {c |}{col 20}{res}{space 2}-.5575633{col 32}{space 2} 1.049155{col 43}{space 1}   -0.53{col 52}{space 3}0.595{col 60}{space 4} -2.61387{col 73}{space 3} 1.498743
{txt}{space 15}48  {c |}{col 20}{res}{space 2}-.5313327{col 32}{space 2}  .487673{col 43}{space 1}   -1.09{col 52}{space 3}0.276{col 60}{space 4}-1.487154{col 73}{space 3} .4244889
{txt}{space 15}49  {c |}{col 20}{res}{space 2}-2.153865{col 32}{space 2} .6004357{col 43}{space 1}   -3.59{col 52}{space 3}0.000{col 60}{space 4}-3.330698{col 73}{space 3}-.9770332
{txt}{space 15}50  {c |}{col 20}{res}{space 2}-.2697264{col 32}{space 2} .7462447{col 43}{space 1}   -0.36{col 52}{space 3}0.718{col 60}{space 4}-1.732339{col 73}{space 3} 1.192886
{txt}{space 18} {c |}
{space 14}year {c |}
{space 13}2003  {c |}{col 20}{res}{space 2} .8168779{col 32}{space 2} .2997942{col 43}{space 1}    2.72{col 52}{space 3}0.006{col 60}{space 4}  .229292{col 73}{space 3} 1.404464
{txt}{space 13}2004  {c |}{col 20}{res}{space 2} .9496187{col 32}{space 2} .4636918{col 43}{space 1}    2.05{col 52}{space 3}0.041{col 60}{space 4} .0407995{col 73}{space 3} 1.858438
{txt}{space 13}2005  {c |}{col 20}{res}{space 2} .6735859{col 32}{space 2} .6024677{col 43}{space 1}    1.12{col 52}{space 3}0.264{col 60}{space 4} -.507229{col 73}{space 3} 1.854401
{txt}{space 13}2006  {c |}{col 20}{res}{space 2}  .536756{col 32}{space 2} .5865944{col 43}{space 1}    0.92{col 52}{space 3}0.360{col 60}{space 4} -.612948{col 73}{space 3}  1.68646
{txt}{space 13}2007  {c |}{col 20}{res}{space 2} .7530891{col 32}{space 2} .5707409{col 43}{space 1}    1.32{col 52}{space 3}0.187{col 60}{space 4}-.3655425{col 73}{space 3} 1.871721
{txt}{space 13}2008  {c |}{col 20}{res}{space 2} .9817217{col 32}{space 2} .4775904{col 43}{space 1}    2.06{col 52}{space 3}0.040{col 60}{space 4} .0456616{col 73}{space 3} 1.917782
{txt}{space 13}2009  {c |}{col 20}{res}{space 2} 1.717244{col 32}{space 2} .3241519{col 43}{space 1}    5.30{col 52}{space 3}0.000{col 60}{space 4} 1.081918{col 73}{space 3}  2.35257
{txt}{space 13}2010  {c |}{col 20}{res}{space 2} 1.841774{col 32}{space 2}  .483484{col 43}{space 1}    3.81{col 52}{space 3}0.000{col 60}{space 4}  .894163{col 73}{space 3} 2.789385
{txt}{space 13}2011  {c |}{col 20}{res}{space 2} 1.740086{col 32}{space 2} .5048845{col 43}{space 1}    3.45{col 52}{space 3}0.001{col 60}{space 4} .7505307{col 73}{space 3} 2.729642
{txt}{space 13}2012  {c |}{col 20}{res}{space 2} 1.469144{col 32}{space 2} .4553786{col 43}{space 1}    3.23{col 52}{space 3}0.001{col 60}{space 4} .5766187{col 73}{space 3}  2.36167
{txt}{space 13}2013  {c |}{col 20}{res}{space 2} 1.590187{col 32}{space 2} .4216662{col 43}{space 1}    3.77{col 52}{space 3}0.000{col 60}{space 4} .7637367{col 73}{space 3} 2.416638
{txt}{space 13}2014  {c |}{col 20}{res}{space 2} 1.132462{col 32}{space 2} .4231693{col 43}{space 1}    2.68{col 52}{space 3}0.007{col 60}{space 4}  .303065{col 73}{space 3} 1.961858
{txt}{space 13}2015  {c |}{col 20}{res}{space 2} 1.039687{col 32}{space 2} .6495646{col 43}{space 1}    1.60{col 52}{space 3}0.109{col 60}{space 4}-.2334362{col 73}{space 3}  2.31281
{txt}{space 13}2016  {c |}{col 20}{res}{space 2} .5884264{col 32}{space 2} .7350576{col 43}{space 1}    0.80{col 52}{space 3}0.423{col 60}{space 4}-.8522601{col 73}{space 3} 2.029113
{txt}{space 13}2017  {c |}{col 20}{res}{space 2} .5871708{col 32}{space 2} .8101553{col 43}{space 1}    0.72{col 52}{space 3}0.469{col 60}{space 4}-1.000704{col 73}{space 3} 2.175046
{txt}{space 13}2018  {c |}{col 20}{res}{space 2}  .929715{col 32}{space 2} .5999964{col 43}{space 1}    1.55{col 52}{space 3}0.121{col 60}{space 4}-.2462565{col 73}{space 3} 2.105686
{txt}{space 13}2019  {c |}{col 20}{res}{space 2} .6697851{col 32}{space 2}  .673651{col 43}{space 1}    0.99{col 52}{space 3}0.320{col 60}{space 4}-.6505466{col 73}{space 3} 1.990117
{txt}{space 13}2020  {c |}{col 20}{res}{space 2} 2.331623{col 32}{space 2} .4835922{col 43}{space 1}    4.82{col 52}{space 3}0.000{col 60}{space 4} 1.383799{col 73}{space 3} 3.279446
{txt}{space 13}2021  {c |}{col 20}{res}{space 2} 1.993963{col 32}{space 2} .6183569{col 43}{space 1}    3.22{col 52}{space 3}0.001{col 60}{space 4} .7820056{col 73}{space 3}  3.20592
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}-12.36466{col 32}{space 2} 7.970675{col 43}{space 1}   -1.55{col 52}{space 3}0.121{col 60}{space 4}-27.98689{col 73}{space 3}  3.25758
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       952{col 28}        .{col 39}-16729.77{col 50}    31{col 58} 33521.53{col 69} 33672.15
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] IC note}}.{p_end}

{com}. 
. *
. estimate store m4
{txt}
{com}. *
. *
. *
. *
. *
. *
. 
. estimates dir

{txt}{hline 13}{c TT}{hline 58}
             {c |}           Dependent  Number of        
        Name {c |} Command    variable     param.  Title 
{hline 13}{c +}{hline 58}
{ralign 12:{stata estimates replay m1:m1}}{col 14}{txt:{c |}}{res}{col 16}{lalign 10:glm}{col 26}{ralign 9:sai_detamt~l}{col 42}  81{col 48}{it:Generalized linear models}
{txt}{ralign 12:{stata estimates replay m2:m2}}{col 14}{txt:{c |}}{res}{col 16}{lalign 10:glm}{col 26}{ralign 9:sai_detamt~l}{col 42}  79{col 48}{it:Generalized linear models}
{txt}{ralign 12:{stata estimates replay m3:m3}}{col 14}{txt:{c |}}{res}{col 16}{lalign 10:glm}{col 26}{ralign 9:sai_detamt~l}{col 42}  82{col 48}{it:Generalized linear models}
{txt}{ralign 12:{stata estimates replay m4:m4}}{col 14}{txt:{c |}}{res}{col 16}{lalign 10:glm}{col 26}{ralign 9:sai_detamt~l}{col 42}  80{col 48}{it:Generalized linear models}
{txt}{hline 13}{c BT}{hline 58}

{com}. 
. 
. 
. 
. 
. ***** WITHIN-OVERPAYMENT ERROR DETECTION ******
. 
. 
. *** FIGURE 2 [MIDDLE PORTION: PARTISAN DISTINCTIONS]: UNCONDITIONAL MARGINAL PARTISAN GUBERNTORIAL APPOINTMENT EFFECTS [REPUBLICAN/DEMOCRATIC GOVERNORS WITH DIRECT APPOINTMENT AUTHORITY]: *** 
. macro list _all
{txt}{p 0 16}
GLIST:{space 10}{res}{res}SGLM_s1 SGLM_ph SGLM_running SGLM_lf SGLM_lt SGLM_mu SGLM_vf SGLM_vt SGLM_f SGLM_p SGLM_a SGLM_m SGLM_y SGLM_L SGLM_V
{p_end}
{txt}{p 0 16}
amtiqr_m2m4_rescaled3_2:{break}
{res}3670542
{p_end}
{txt}{p 0 16}
amtiqr_m1m3_rescaled3_2:{break}
{res}3669034
{p_end}
{txt}{p 0 16}
amtiqr_m2m4_rescaled3_1:{break}
{res}3032901
{p_end}
{txt}{p 0 16}
amtiqr_m1m3_rescaled3_1:{break}
{res}3016376
{p_end}
{txt}{p 0 16}
S_2:{space 12}{res}{res}287194.6245340402
{p_end}
{txt}{p 0 16}
S_1:{space 12}{res}{res}401711.5437554451
{p_end}
{txt}{p 0 16}
ctiqr_m2m4_rescaled3_1or2:{break}
{res}4310
{p_end}
{txt}{p 0 16}
ctiqr_m2m4_rescaled3_2:{break}
{res}4008
{p_end}
{txt}{p 0 16}
ctiqr_m2m4_rescaled3_1:{break}
{res}5163
{p_end}
{txt}{p 0 16}
ctiqr_m1m3_rescaled3_1or2:{break}
{res}4278
{p_end}
{txt}{p 0 16}
ctiqr_m1m3_rescaled3_2:{break}
{res}3943
{p_end}
{txt}{p 0 16}
ctiqr_m1m3_rescaled3_1:{break}
{res}5163
{p_end}
{txt}{p 0 16}
ctiqr_m2m4_rescaled4_1:{break}
{res}4310
{p_end}
{txt}{p 0 16}
ctiqr_m1m3_rescaled4_1:{break}
{res}4278
{p_end}
{txt}{p 0 16}
amtiqr_m2m4_rescaled4_1:{break}
{res}3222368
{p_end}
{txt}{p 0 16}
amtiqr_m1m3_rescaled4_1:{break}
{res}3272651
{p_end}
{txt}{p 0 16}
S_level:{space 8}{res}{res}95
{p_end}
{txt}{p 0 16}
F1:{space 13}{res}{res}help advice;
{p_end}
{txt}{p 0 16}
F2:{space 13}{res}{res}describe;
{p_end}
{txt}{p 0 16}
F7:{space 13}{res}{res}save 
{p_end}
{txt}{p 0 16}
F8:{space 13}{res}{res}use 
{p_end}
{txt}{p 0 16}
S_ADO:{space 10}{res}{res}BASE;SITE;.;PERSONAL;PLUS;OLDPLACE
{p_end}
{txt}{p 0 16}
S_StataMP:{space 6}{res}{res}MP
{p_end}
{txt}{p 0 16}
S_StataSE:{space 6}{res}{res}SE
{p_end}
{txt}{p 0 16}
S_OS:{space 11}{res}{res}Windows
{p_end}
{txt}{p 0 16}
S_OSDTL:{space 8}{res}{res}64-bit
{p_end}
{txt}{p 0 16}
S_MACH:{space 9}{res}{res}PC (64-bit x86-64)
{p_end}
{txt}{p 0 16}
S_FN:{space 11}{res}{res}C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\Data Replication File Materials (JPART)\Data\Admin_Bias.MANUSCRIPT DATABASE.07-07-2025.dta
{p_end}
{txt}{p 0 16}
S_FNDATE:{space 7}{res}{res} 1 Feb 2026 15:11
{p_end}
{txt}
{com}. 
. estimate restore m3
{txt}(results {stata estimates replay m3:m3} are active now)

{com}. lincom 1.gubapptauth_partisan_rescaled3*$amtiqr_m1m3_rescaled3_1

{p 0 7}{space 1}{text:( 1)}{space 1} {res}3016376{res}*{res}[sai_detamt_clmtreal]1.gubapptauth_partisan_rescaled3 = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}sai_detamt~l{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 795652.1{col 26}{space 2} 416285.1{col 37}{space 1}    1.91{col 46}{space 3}0.056{col 54}{space 4}-20251.67{col 67}{space 3}  1611556
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. matrix m3b1 = 4, 2, 1, r(estimate), r(lb), r(ub) , r(p)
{txt}
{com}. *
. 
. di $ctiqr_m1m3_rescaled3_1
{res}5163
{txt}
{com}. di r(estimate)/$ctiqr_m1m3_rescaled3_1
{res}154.10656
{txt}
{com}. *
. *
. *
. *
. estimate restore m4
{txt}(results {stata estimates replay m4:m4} are active now)

{com}. lincom 1.gubapptauth_partisan_rescaled3*$amtiqr_m2m4_rescaled3_1 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}3032901{res}*{res}[sai_detamt_clmtreal]1.gubapptauth_partisan_rescaled3 = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}sai_detamt~l{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 831464.7{col 26}{space 2} 420128.3{col 37}{space 1}    1.98{col 46}{space 3}0.048{col 54}{space 4} 8028.365{col 67}{space 3}  1654901
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. matrix m4b1 = 5, 2, 2, r(estimate), r(lb), r(ub) , r(p)
{txt}
{com}. *
. di $ctiqr_m2m4_rescaled3_1
{res}5163
{txt}
{com}. di r(estimate)/$ctiqr_m2m4_rescaled3_1
{res}161.04294
{txt}
{com}. 
. *
. *
. *
. *
. *
. *
. estimate restore m3
{txt}(results {stata estimates replay m3:m3} are active now)

{com}. lincom 2.gubapptauth_partisan_rescaled3*$amtiqr_m1m3_rescaled3_2

{p 0 7}{space 1}{text:( 1)}{space 1} {res}3669034{res}*{res}[sai_detamt_clmtreal]2.gubapptauth_partisan_rescaled3 = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}sai_detamt~l{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} -1851056{col 26}{space 2}  1382132{col 37}{space 1}   -1.34{col 46}{space 3}0.180{col 54}{space 4} -4559985{col 67}{space 3} 857873.8
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix m3b2 = 7, 3, 1, r(estimate), r(lb), r(ub) , r(p)
{txt}
{com}. *
. di $ctiqr_m1m3_rescaled3_2
{res}3943
{txt}
{com}. di r(estimate)/$ctiqr_m1m3_rescaled3_2
{res}-469.45366
{txt}
{com}. *
. *
. *
. estimate restore m4
{txt}(results {stata estimates replay m4:m4} are active now)

{com}. lincom 2.gubapptauth_partisan_rescaled3*$amtiqr_m2m4_rescaled3_2 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}3670542{res}*{res}[sai_detamt_clmtreal]2.gubapptauth_partisan_rescaled3 = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}sai_detamt~l{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} -1811493{col 26}{space 2}  1377477{col 37}{space 1}   -1.32{col 46}{space 3}0.188{col 54}{space 4} -4511299{col 67}{space 3} 888312.1
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. matrix m4b2 = 8, 3, 2, r(estimate), r(lb), r(ub) , r(p)
{txt}
{com}. *
. di $ctiqr_m2m4_rescaled3_2
{res}4008
{txt}
{com}. di r(estimate)/$ctiqr_m2m4_rescaled3_2
{res}-451.96942
{txt}
{com}. *
. 
. 
. *
. *
. *
. *
. 
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. 
.  
. ***  FIGURE 2 [BOTTOM PORTION: PARTISAN DISTINCTIONS] ANCILLARY WITHIN-OVERPAYMENT ERROR DETECTION HYPOTHESES [REPUBLICAN/DEMOCRATIC GOVERNORS WITH DIRECT APPOINTMENT AUTHORITY] ***  
. *** 
.  
. estimate restore m3
{txt}(results {stata estimates replay m3:m3} are active now)

{com}. lincom 1.gubapptauth_partisan_rescaled3*$amtiqr_m1m3_rescaled3_1   - (2.gubapptauth_partisan_rescaled3*$amtiqr_m1m3_rescaled3_2) 

{p 0 7}{space 1}{text:( 1)}{space 1} {res}3016376{res}*{res}[sai_detamt_clmtreal]1.gubapptauth_partisan_rescaled3 - 3669034{res}*{res}[sai_detamt_clmtreal]2.gubapptauth_partisan_rescaled3 = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}sai_detamt~l{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  2646708{col 26}{space 2}  1038568{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4} 611151.3{col 67}{space 3}  4682265
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. matrix m3b1b2 = 10, 4, 1, r(estimate), r(lb), r(ub) , r(p)
{txt}
{com}. *
. di $ctiqr_m1m3_rescaled3_1or2
{res}4278
{txt}
{com}. di r(estimate)/$ctiqr_m1m3_rescaled3_1or2
{res}618.67881
{txt}
{com}. 
. *
. *
. *
. estimate restore m4
{txt}(results {stata estimates replay m4:m4} are active now)

{com}. lincom 1.gubapptauth_partisan_rescaled3*$amtiqr_m2m4_rescaled3_1    - (2.gubapptauth_partisan_rescaled3*$amtiqr_m2m4_rescaled3_2)

{p 0 7}{space 1}{text:( 1)}{space 1} {res}3032901{res}*{res}[sai_detamt_clmtreal]1.gubapptauth_partisan_rescaled3 - 3670542{res}*{res}[sai_detamt_clmtreal]2.gubapptauth_partisan_rescaled3 = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}sai_detamt~l{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}  2642958{col 26}{space 2}  1034875{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4} 614639.7{col 67}{space 3}  4671276
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. matrix m4b1b2 = 11, 4, 2, r(estimate), r(lb), r(ub) , r(p)
{txt}
{com}. *
. di $ctiqr_m2m4_rescaled3_1or2
{res}4310
{txt}
{com}. di r(estimate)/$ctiqr_m2m4_rescaled3_1or2
{res}613.21534
{txt}
{com}. 
. *
. 
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. *************************************************************************************************************************************************************************************************************
. 
. *
. *
. *
. cd "C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Output Files"
{res}C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Output Files
{txt}
{com}. 
. esttab m1 m2 m3 m4 using ManuscriptTable1.02-01-2026.rtf, ///
> cells(b(star fmt(%9.3f)) se(par) p(fmt(3) par("[" "]"))) starl( * 0.10 ** 0.05 *** 0.010) label ///
> nonumbers mtitles("Model 1" "Model 2" "Model 3" "Model 4")  ///
> stats(year qic pseudor chi2 N, fmt(2) label("Year Fixed Effects" "QIC" "Pseudo R-Squared" "Wald" "N")) append
{res}{txt}{p 0 4 2}
(file {bf}
ManuscriptTable1.02-01-2026.rtf{rm}
not found)
{p_end}
(output written to {browse  `"ManuscriptTable1.02-01-2026.rtf"'})

{com}. 
. 
. 
. 
. 
. 
. 
. 
. 
. **# PUTEXCEL: ESTIMATES FOR FIGURE 1 DEPENDENT VARIABLE CATEGORIES
. 
. tab gubapptauth_partisan_rescaled4 if !missing(clmterror_est), matcell(rescaled4)

{txt}gubapptauth {c |}
_partisan_r {c |}
   escaled4 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        172       17.22       17.22
{txt}          1 {c |}{res}        827       82.78      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        999      100.00
{txt}
{com}. tab gubapptauth_partisan_rescaled3 if !missing(clmterror_est), matcell(rescaled3)

{txt}gubapptauth {c |}
_partisan_r {c |}
   escaled3 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        172       17.22       17.22
{txt}          1 {c |}{res}        425       42.54       59.76
{txt}          2 {c |}{res}        402       40.24      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        999      100.00
{txt}
{com}. 
. matrix rescaled4a= rescaled4, rescaled4/10, (1\2)
{txt}
{com}. matrix row3= ., ., 3
{txt}
{com}. matrix rescaled3a= rescaled3, rescaled3/10, (4\5\6)
{txt}
{com}. 
. matrix fig1 = rescaled4a\row3\rescaled3a
{txt}
{com}. matrix colnames fig1= freq proportion row
{txt}
{com}. 
. putexcel set "C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Graphics Files\Fig1.02-01-2026.xlsx", sheet("figure2") replace
{res}{p}{txt}{txt}note: file will be replaced when the first {cmd:putexcel} command is issued.{p_end}

{com}. putexcel A1= matrix(fig1), colnames 
{res}{txt}file {bf:C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Graphics Files\Fig1.02-01-2026.xlsx} saved

{com}. 
. **# PUTEXCEL: ESTIMATES FOR FIGURE 2
. matrix row3= 3, ., ., ., ., ., .
{txt}
{com}. matrix row6= 6, ., ., ., ., ., .
{txt}
{com}. matrix row9= 9, ., ., ., ., ., .
{txt}
{com}. 
. matrix fig2 = m1a\ m1d\ row3 \ m3b1\ m4b1\ row6\ m3b2\m4b2\row9\ m3b1b2\m4b1b2
{txt}
{com}. 
. matrix colnames fig2= row partisan group estimates lb ub p-value
{txt}
{com}. matrix list fig2
{res}
{txt}fig2[11,7]
           row    partisan       group   estimates          lb          ub
r1 {res}          1           1           1   379099.58  -197689.97   955889.13
{txt}r1 {res}          2           1           2   401711.54  -161179.58   964602.66
{txt}r1 {res}          3           .           .           .           .           .
{txt}r1 {res}          4           2           1   795652.15  -20251.667     1611556
{txt}r1 {res}          5           2           2    831464.7   8028.3652     1654901
{txt}r1 {res}          6           .           .           .           .           .
{txt}r1 {res}          7           3           1  -1851055.8  -4559985.3   857873.75
{txt}r1 {res}          8           3           2  -1811493.4  -4511298.9   888312.11
{txt}r1 {res}          9           .           .           .           .           .
{txt}r1 {res}         10           4           1   2646707.9   611151.26   4682264.6
{txt}r1 {res}         11           4           2   2642958.1   614639.74   4671276.5

{txt}       p-value
r1 {res}  .19767562
{txt}r1 {res}  .16188999
{txt}r1 {res}          .
{txt}r1 {res}  .05596406
{txt}r1 {res}  .04780775
{txt}r1 {res}          .
{txt}r1 {res}  .18048104
{txt}r1 {res}  .18848282
{txt}r1 {res}          .
{txt}r1 {res}  .01082123
{txt}r1 {res}  .01065268
{reset}
{com}. 
. putexcel set "C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Graphics Files\Fig2.02-01-2026.xlsx", sheet("lincomresults") replace
{res}{p}{txt}{txt}note: file will be replaced when the first {cmd:putexcel} command is issued.{p_end}

{com}. putexcel A1= matrix(fig2), colnames 
{res}{txt}file {bf:C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Graphics Files\Fig2.02-01-2026.xlsx} saved

{com}. 
. *
. *
. *
. *
. *
. 
. *******************************************************************************
. ** Set Default Graphics Settings
. set scheme stcolor, permanently
{txt}({cmd:set scheme} preference recorded)

{com}. graph set svg fontface "Cambria Math"
{txt}
{com}. 
. 
. *******************************************************************************
. *******************************************************************************
. *******************************************************************************
. *******************************************************************************
. 
. 
. 
. **# Generate Graph "Figure 1"
. import excel "C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Graphics Files\Fig1.02-01-2026.xlsx", firstrow clear
{res}{text}(3 vars, 6 obs)

{com}. 
. graph set window fontface "Cambria Math"
{txt}
{com}. set scheme stcolor
{txt}
{com}. 
. twoway (bar proportion row,  yaxis(1) ysc(r(0 90)) ytitle("Proportion of Each Category (%)", margin(medium) size(medsmall)) ylabel(0 "0" 20 "20" 40 "40" 60 "60" 80 "80", labsize(small))) ///
> (scatter freq row,  msymbol(none) yaxis(2)  ylabel(, axis(2) labsize(small)) ytitle("Counts", margin(medium) axis(2)) msymbol(none)), ///
> text(19 1 "172 (17.2%)", place(n) size(small)) ///
> text(84.6 2 "827 (82.8%)", place(n) size(small)) ///
> text(19 4 "172 (17.2%)", place(n) size(small)) ///
> text(44.4 5 "425 (42.6%)", place(n) size(small)) ///
> text(42 6 "402 (40.2%)", place(n) size(small)) ///
> xlabel(1 "Non-Gov Direct Appointment" 2 "Gov Direct Appointment" 4 "Non-Gov Direct Appointment" 5 "Republican Gov Direct Appointment" 6 "Democratic Gov Direct Appointment",  angle(40) noticks labsize(medsmall)) ///
> title("{c -(}bf: Figure 1. Distribution of Appointment Authority{c )-}" "(Unemployment Insurance Agency Heads in the American States, 2002-2021)", size(medsmall) span margin(medsmall)) graphregion(margin(l+11 r)) xsize(7) legend(off) xtitle("")
{res}{txt}
{com}. 
. graph save "C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Graphics Files\Fig1-02-01-2026", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Graphics Files\Fig1-02-01-2026.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Graphics Files\Fig1-02-01-2026.gph} saved

{com}. 
. 
. *******************************************************************************
. *******************************************************************************
. *******************************************************************************
. *******************************************************************************
. 
. 
. **# Generate Graph "Figure 2"
. import excel "C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Graphics Files\Fig2.02-01-2026.xlsx", firstrow clear
{res}{text}(7 vars, 11 obs)

{com}. 
. graph set window fontface "Cambria Math"
{txt}
{com}. set scheme stcolor
{txt}
{com}. 
. twoway (rcap lb ub row, lpattern(solid) horizontal) ///
> (scatter row estimates if partisan==1 & group==1, mcolor(black) msymbol(square) msize(small)) ///
> (scatter row estimates if partisan==1 & group==2, mcolor(black) msymbol(square_hollow) msize(small) ) ///
> (scatter row estimates  if partisan==2 & group==1, mcolor(red) msymbol(square) msize(small)) ///
> (scatter row estimates  if partisan==2 & group==2, mcolor(red) msymbol(square_hollow) msize(small)) ///
> (scatter row estimates if partisan==3 & group==1, mcolor(blue) msymbol(square) msize(small)) ///
> (scatter row estimates if partisan==3 & group==2, mcolor(blue) msymbol(square_hollow) msize(small)) ///
> (scatter row estimates if partisan==4 & group==1, mcolor(purple) msymbol(square) msize(small)) ///
> (scatter row estimates if partisan==4 & group==2, mcolor(purple) msymbol(square_hollow) msize(small)) ///
> , xline(0, lcolor(red) lpattern(shortdash))  ///
> ylabel(1 "{c -(}bf: Gov. Direct Appointment (Binary){c )-}"  3.3 `" "{c -(}bf: Partisan Distinctions{c )-}" "' 4.5 "Republican Gov. Direct Appointment" 7.5 "Democratic Gov. Direct Appointment" 10.5 `" "Republican - Democratic Gov." "Direct Appointment Difference" "' ///
> ,angle(0) labsize(medsmall) nogrid noticks) ///
> legend(order(2 "Benefit Overpayment Error (Full Sample)" 3 "Benefit Overpayment Error (Restricted Sample)") pos(6) ring(2) cols(1) size(small))  ///
> xlabel(-4000000 "-$4" -2000000 "-$2" 0 "$0" 2000000 "$2" 4000000 "$4", labsize(medium)) ///
> yscale(reverse) graphregion(margin(l=3 r=5)) ysize(6) xsize(8.5)  ///
> title(" {c -(}bf:Figure 2. Appointment Authority Effects on{c )-}" "{c -(}bf: Benefit Overpayment Error Detection{c )-}", size(medium) span) ///
> subtitle("(Unemployment Insurance Agency Heads in the American States, 2002-2021)", size(medsmall) span margin(b+4)) ///
> xtitle("Agency Initiated Benefit Overpayment Detection", size(medsmall) margin(medium)) ytitle("") ///
> note("Note: Dollar Amounts Presented in 2010 Constant Million Dollars.", pos(7))
{res}{txt}
{com}. 
. 
. graph save "C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Graphics Files\Fig2-02-01-2026", replace
{txt}{p 0 4 2}
(file {bf}
C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Graphics Files\Fig2-02-01-2026.gph{rm}
not found)
{p_end}
{res}{txt}file {bf:C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\JPART R&R\Statistics\Graphics Files\Fig2-02-01-2026.gph} saved

{com}. 
. 
. 
. 
. 
. 
. 
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\hongj\Dropbox\Administration of UI Programs in the American States (Ji-Hyeun Hong)\Administrative Inequality Bias (Paper #1)\Data Replication File Materials (JPART)\Output\Administrative Bias.MANUSCRIPT.02-01-2026.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 1 Feb 2026, 15:11:37
{txt}{.-}
{smcl}
{txt}{sf}{ul off}